Characteristic and factors of competitive maritime industry clusters in Indonesia

NASA Astrophysics Data System (ADS)

Marlyana, N.; Tontowi, A. E.; Yuniarto, H. A.

2017-12-01

Indonesia is situated in the strategic position between two oceans therefore is identified as a maritime state. The fact opens big opportunity to build a competitive maritime industry. However, potential factors to boost the competitive maritime industry still need to be explored. The objective of this paper is then to determine the main characteristics and potential factors of competitive maritime industry cluster. Qualitative analysis based on literature review has been carried out in two aspects. First, benchmarking analysis conducted to distinguish the most relevant factors of maritime clusters in several countries in Europe (Norway, Spain, South West of England) and Asia (China, South Korea, Malaysia). Seven key dimensions are used for this benchmarking. Secondly, the competitiveness of maritime clusters in Indonesia was diagnosed through a reconceptualization of Porter’s Diamond model. There were four interlinked of advanced factors in and between companies within clusters, which can be influenced in a proactive way by government.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks.

PubMed

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R, and the bivariate recurrence network measure, the average cross-clustering coefficient C(cross), can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L, and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient C(cross) is independent of the outcome of the randomness test based on the average clustering coefficient C. Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks

NASA Astrophysics Data System (ADS)

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R , and the bivariate recurrence network measure, the average cross-clustering coefficient Ccross, can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L , and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient Ccross is independent of the outcome of the randomness test based on the average clustering coefficient C . Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Hebbian self-organizing integrate-and-fire networks for data clustering.

PubMed

Landis, Florian; Ott, Thomas; Stoop, Ruedi

2010-01-01

We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

PubMed Central

Guerra, Luis; McGarry, Laura M; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2011-01-01

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011 PMID:21154911

Cluster Analysis of Longidorus Species (Nematoda: Longidoridae), a New Approach in Species Identification

PubMed Central

Ye, Weimin; Robbins, R. T.

2004-01-01

Hierarchical cluster analysis based on female morphometric character means including body length, distance from vulva opening to anterior end, head width, odontostyle length, esophagus length, body width, tail length, and tail width were used to examine the morphometric relationships and create dendrograms for (i) 62 populations belonging to 9 Longidorus species from Arkansas, (ii) 137 published Longidorus species, and (iii) 137 published Longidorus species plus 86 populations of 16 Longidorus species from Arkansas and various other locations by using JMP 4.02 software (SAS Institute, Cary, NC). Cluster analysis dendograms visually illustrated the grouping and morphometric relationships of the species and populations. It provided a computerized statistical approach to assist by helping to identify and distinguish species, by indicating morphometric relationships among species, and by assisting with new species diagnosis. The preliminary species identification can be accomplished by running cluster analysis for unknown species together with the data matrix of known published Longidorus species. PMID:19262809

Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

ERIC Educational Resources Information Center

Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David

2013-01-01

Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…

Food Purchase Decision-Making Typologies of Women with Non-Insulin-Dependent Diabetes Mellitus.

ERIC Educational Resources Information Center

Miller, Carla; Warland, Rex; Achterberg, Cheryl

1997-01-01

Food selection is a key factor in the nutritional management of diabetes. Criteria that influence point-of-purchase decision making in women with non-insulin-dependent diabetes mellitus were identified. Four types of shoppers were distinguished from interviews; cluster analysis was used to confirm the analysis. Usefulness in patient education is…

Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants

NASA Astrophysics Data System (ADS)

Atherton, Daniel

Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher's LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the most mildly diseased plants. Of the analysis methods attempted, cluster analysis and vegetative indices were the most promising. The results show the potential of hyperspectral remote sensing for the detection of early blight in potato plants.

Sensory analysis of characterising flavours: evaluating tobacco product odours using an expert panel.

PubMed

Krüsemann, Erna J Z; Lasschuijt, Marlou P; de Graaf, C; de Wijk, René A; Punter, Pieter H; van Tiel, Loes; Cremers, Johannes W J M; van de Nobelen, Suzanne; Boesveldt, Sanne; Talhout, Reinskje

2018-05-23

Tobacco flavours are an important regulatory concept in several jurisdictions, for example in the USA, Canada and Europe. The European Tobacco Products Directive 2014/40/EU prohibits cigarettes and roll-your-own tobacco having a characterising flavour. This directive defines characterising flavour as 'a clearly noticeable smell or taste other than one of tobacco […]'. To distinguish between products with and without a characterising flavour, we trained an expert panel to identify characterising flavours by smelling. An expert panel (n=18) evaluated the smell of 20 tobacco products using self-defined odour attributes, following Quantitative Descriptive Analysis. The panel was trained during 14 attribute training, consensus training and performance monitoring sessions. Products were assessed during six test sessions. Principal component analysis, hierarchical clustering (four and six clusters) and Hotelling's T-tests (95% and 99% CIs) were used to determine differences and similarities between tobacco products based on odour attributes. The final attribute list contained 13 odour descriptors. Panel performance was sufficient after 14 training sessions. Products marketed as unflavoured that formed a cluster were considered reference products. A four-cluster method distinguished cherry-flavoured, vanilla-flavoured and menthol-flavoured products from reference products. Six clusters subdivided reference products into tobacco leaves, roll-your-own and commercial products. An expert panel was successfully trained to assess characterising odours in cigarettes and roll-your-own tobacco. This method could be applied to other product types such as e-cigarettes. Regulatory decisions on the choice of reference products and significance level are needed which directly influences the products being assessed as having a characterising odour. © 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.

Global optimization of small bimetallic Pd-Co binary nanoalloy clusters: a genetic algorithm approach at the DFT level.

PubMed

Aslan, Mikail; Davis, Jack B A; Johnston, Roy L

2016-03-07

The global optimisation of small bimetallic PdCo binary nanoalloys are systematically investigated using the Birmingham Cluster Genetic Algorithm (BCGA). The effect of size and composition on the structures, stability, magnetic and electronic properties including the binding energies, second finite difference energies and mixing energies of Pd-Co binary nanoalloys are discussed. A detailed analysis of Pd-Co structural motifs and segregation effects is also presented. The maximal mixing energy corresponds to Pd atom compositions for which the number of mixed Pd-Co bonds is maximised. Global minimum clusters are distinguished from transition states by vibrational frequency analysis. HOMO-LUMO gap, electric dipole moment and vibrational frequency analyses are made to enable correlation with future experiments.

The Adjustment Disorder--New Module 20 as a Screening Instrument: Cluster Analysis and Cut-off Values.

PubMed

Lorenz, L; Bachem, R C; Maercker, A

2016-10-01

Adjustment disorder (AjD) is a transient mental health condition emerging after stressful life events. Its diagnostic criteria have recently been under revision which led to the development of the Adjustment Disorder--New Module 20 (ADNM-20) as a self-report assessment. To identify a threshold value for people at high risk for AjD. As part of a randomized controlled trial evaluating a self-help manual for burglary victims, the baseline data of all participants (n=80) were analyzed. Besides the ADNM-20, participants answered self-report questionnaires regarding the external variables post-traumatic stress disorder symptomatology, depression, anxiety, and stress levels. We used cluster analysis and ROC analysis to identify the most appropriate cut-off value. The cluster analysis identified three different subgroups. They differed in their level of AjD symptomatology from low to high symptom severity. The same pattern of impairment was found for the external variables. The ROC analysis testing the ADNM-20 sum scoreagainst the theory-based diagnostic algorithm, revealed an optimal cut-off score at 47.5 to distinguish between people at high risk for AjD and people at low risk. The ADNM-20 distinguishes between people with low, moderate, and high symptomatology. The recommendation for a cut-off score at 47.5 facilitates the use of the ADNM-20 in research and practice.

Exploring Different Patterns of Love Attitudes among Chinese College Students.

PubMed

Zeng, Xianglong; Pan, Yiqin; Zhou, Han; Yu, Shi; Liu, Xiangping

2016-01-01

Individual differences in love attitudes and the relationship between love attitudes and other variables in Asian culture lack in-depth exploration. This study conducted cluster analysis with data regarding love attitudes obtained from 389 college students in mainland China. The result of cluster analysis based on love-attitude scales distinguished four types of students: game players, rational lovers, emotional lovers, and absence lovers. These four groups of students showed significant differences in sexual attitudes and personality traits of deliberation and dutifulness but not self-discipline. The study's implications for future studies on love attitudes in certain cultural groups were also discussed.

Correlation analysis of fracture arrangement in space

NASA Astrophysics Data System (ADS)

Marrett, Randall; Gale, Julia F. W.; Gómez, Leonel A.; Laubach, Stephen E.

2018-03-01

We present new techniques that overcome limitations of standard approaches to documenting spatial arrangement. The new techniques directly quantify spatial arrangement by normalizing to expected values for randomly arranged fractures. The techniques differ in terms of computational intensity, robustness of results, ability to detect anti-correlation, and use of fracture size data. Variation of spatial arrangement across a broad range of length scales facilitates distinguishing clustered and periodic arrangements-opposite forms of organization-from random arrangements. Moreover, self-organized arrangements can be distinguished from arrangements due to extrinsic organization. Traditional techniques for analysis of fracture spacing are hamstrung because they account neither for the sequence of fracture spacings nor for possible coordination between fracture size and position, attributes accounted for by our methods. All of the new techniques reveal fractal clustering in a test case of veins, or cement-filled opening-mode fractures, in Pennsylvanian Marble Falls Limestone. The observed arrangement is readily distinguishable from random and periodic arrangements. Comparison of results that account for fracture size with results that ignore fracture size demonstrates that spatial arrangement is dominated by the sequence of fracture spacings, rather than coordination of fracture size with position. Fracture size and position are not completely independent in this example, however, because large fractures are more clustered than small fractures. Both spatial and size organization of veins here probably emerged from fracture interaction during growth. The new approaches described here, along with freely available software to implement the techniques, can be applied with effect to a wide range of structures, or indeed many other phenomena such as drilling response, where spatial heterogeneity is an issue.

Distant star clusters of the Milky Way in MOND

NASA Astrophysics Data System (ADS)

Haghi, H.; Baumgardt, H.; Kroupa, P.

2011-03-01

We determine the mean velocity dispersion of six Galactic outer halo globular clusters, AM 1, Eridanus, Pal 3, Pal 4, Pal 15, and Arp 2 in the weak acceleration regime to test classical vs. modified Newtonian dynamics (MOND). Owing to the nonlinearity of MOND's Poisson equation, beyond tidal effects, the internal dynamics of clusters is affected by the external field in which they are immersed. For the studied clusters, particle accelerations are much lower than the critical acceleration a0 of MOND, but the motion of stars is neither dominated by internal accelerations (ai ≫ ae) nor external accelerations (ae ≫ ai). We use the N-body code N-MODY in our analysis, which is a particle-mesh-based code with a numerical MOND potential solver developed by Ciotti et al. (2006, ApJ, 640, 741) to derive the line-of-sight velocity dispersion by adding the external field effect. We show that Newtonian dynamics predicts a low-velocity dispersion for each cluster, while in modified Newtonian dynamics the velocity dispersion is much higher. We calculate the minimum number of measured stars necessary to distinguish between Newtonian gravity and MOND with the Kolmogorov-Smirnov test. We also show that for most clusters it is necessary to measure the velocities of between 30 to 80 stars to distinguish between both cases. Therefore the observational measurement of the line-of-sight velocity dispersion of these clusters will provide a test for MOND.

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

PubMed

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

2014-11-01

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

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

NASA Astrophysics Data System (ADS)

Chang, Bingguo; Chen, Xiaofei

2018-05-01

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

Side scatter versus CD45 flow cytometric plot can distinguish acute leukaemia subtypes.

PubMed

Saksena, Annapurna; Gautam, Parul; Desai, Parth; Gupta, Naresh; Dubey, A P; Singh, Tejinder

2016-05-01

Flow cytometry is an important tool to diagnose acute leukaemia. Attempts are being made to find the minimal number of antibodies for correctly diagnosing acute leukaemia subtypes. The present study was designed to evaluate the analysis of side scatter (SSC) versus CD45 flow dot plot to distinguish acute myeloid leukaemia (AML) from acute lymphoblastic leukaemia (ALL), with minimal immunological markers. One hundred consecutive cases of acute leukaemia were evaluated for blast cluster on SSC versus CD45 plots. The parameters studied included visual shape, CD45 and side scatter expression, continuity with residual granulocytes/lymphocytes/monocytes and ratio of maximum width to maximum height (w/h). The final diagnosis of ALL and AML and their subtypes was made by morphology, cytochemistry and immunophenotyping. Two sample Wilcoxon rank-sum (Mann Whitney) test and Kruskal-Wallis equality-of-populations rank tests were applied to elucidate the significance of the above ratios of blast cluster for diagnosis of ALL, AML and their subtypes. Receiver operating characteristic (ROC) curves were generated and the optimal cut-offs of the w/h ratio to distinguish between ALL and AML determined. Of the 100 cases, 57 of ALL and 43 cases of AML were diagnosed. The median w/h ratio of blast population was 3.8 for ALL and 1 for AML (P<0.001). ROC had area under curve of 0.9772.The optimal cut-off of the w/h ratio for distinction of ALL from AML was found to be 1.6. Our findings suggest that if w/h ratio on SSC versus CD45 plot is less than 1.6, AML may be considered, and if it is more than 1.6, ALL may be diagnosed. Using morphometric analysis of the blast cluster on SSC versus CD45, it was possible to distinguish between ALL and AML, and their subtypes.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Structure-related clustering of gene expression fingerprints of thp-1 cells exposed to smaller polycyclic aromatic hydrocarbons.

PubMed

Wan, B; Yarbrough, J W; Schultz, T W

2008-01-01

This study was undertaken to test the hypothesis that structurally similar PAHs induce similar gene expression profiles. THP-1 cells were exposed to a series of 12 selected PAHs at 50 microM for 24 hours and gene expressions profiles were analyzed using both unsupervised and supervised methods. Clustering analysis of gene expression profiles revealed that the 12 tested chemicals were grouped into five clusters. Within each cluster, the gene expression profiles are more similar to each other than to the ones outside the cluster. One-methylanthracene and 1-methylfluorene were found to have the most similar profiles; dibenzothiophene and dibenzofuran were found to share common profiles with fluorine. As expression pattern comparisons were expanded, similarity in genomic fingerprint dropped off dramatically. Prediction analysis of microarrays (PAM) based on the clustering pattern generated 49 predictor genes that can be used for sample discrimination. Moreover, a significant analysis of Microarrays (SAM) identified 598 genes being modulated by tested chemicals with a variety of biological processes, such as cell cycle, metabolism, and protein binding and KEGG pathways being significantly (p < 0.05) affected. It is feasible to distinguish structurally different PAHs based on their genomic fingerprints, which are mechanism based.

Molecular Clustering Interrelationships and Carbohydrate Conformation in Hull and Seeds Among Barley Cultivars

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

N Liu; P Yu

2011-12-31

The objective of this study was to use molecular spectral analyses with the diffuse reflectance Fourier transform infrared spectroscopy (DRIFT) bioanlytical technique to study carbohydrate conformation features, molecular clustering and interrelationships in hull and seed among six barley cultivars (AC Metcalfe, CDC Dolly, McLeod, CDC Helgason, CDC Trey, CDC Cowboy), which had different degradation kinetics in rumen. The molecular structure spectral analyses in both hull and seed involved the fingerprint regions of ca. 1536-1484 cm{sup -1} (attributed mainly to aromatic lignin semicircle ring stretch), ca. 1293-1212 cm{sup -1} (attributed mainly to cellulosic compounds in the hull), ca. 1269-1217 cm{sup -1}more » (attributed mainly to cellulosic compound in the seeds), and ca. 1180-800 cm{sup -1} (attributed mainly to total CHO C-O stretching vibrations) together with an agglomerative hierarchical cluster (AHCA) and principal component spectral analyses (PCA). The results showed that the DRIFT technique plus AHCA and PCA molecular analyses were able to reveal carbohydrate conformation features and identify carbohydrate molecular structure differences in both hull and seeds among the barley varieties. The carbohydrate molecular spectral analyses at the region of ca. 1185-800 cm{sup -1} together with the AHCA and PCA were able to show that the barley seed inherent structures exhibited distinguishable differences among the barley varieties. CDC Helgason had differences from AC Metcalfe, MeLeod, CDC Cowboy and CDC Dolly in carbohydrate conformation in the seed. Clear molecular cluster classes could be distinguished and identified in AHCA analysis and the separate ellipses could be grouped in PCA analysis. But CDC Helgason had no distinguished differences from CDC Trey in carbohydrate conformation. These carbohydrate conformation/structure difference could partially explain why the varieties were different in digestive behaviors in animals. The molecular spectroscopy technique used in this study could also be used for other plant-based feed and food structure studies.« less

Exploring Different Patterns of Love Attitudes among Chinese College Students

PubMed Central

Zeng, Xianglong; Pan, Yiqin; Zhou, Han; Yu, Shi; Liu, Xiangping

2016-01-01

Individual differences in love attitudes and the relationship between love attitudes and other variables in Asian culture lack in-depth exploration. This study conducted cluster analysis with data regarding love attitudes obtained from 389 college students in mainland China. The result of cluster analysis based on love-attitude scales distinguished four types of students: game players, rational lovers, emotional lovers, and absence lovers. These four groups of students showed significant differences in sexual attitudes and personality traits of deliberation and dutifulness but not self-discipline. The study’s implications for future studies on love attitudes in certain cultural groups were also discussed. PMID:27851784

Phylogenetic analysis of different breeds of domestic chickens in selected area of Peninsular Malaysia inferred from partial cytochrome b gene information and RAPD markers.

PubMed

Yap, Fook Choy; Yan, Yap Jin; Loon, Kiung Teh; Zhen, Justina Lee Ning; Kamau, Nelly Warau; Kumaran, Jayaraj Vijaya

2010-10-01

The present investigation was carried out in an attempt to study the phylogenetic analysis of different breeds of domestic chickens in Peninsular Malaysia inferred from partial cytochrome b gene information and random amplified polymorphic DNA (RAPD) markers. Phylogenetic analysis using both neighbor-joining (NJ) and maximum parsimony (MP) methods produced three clusters that encompassed Type-I village chickens, the red jungle fowl subspecies and the Japanese Chunky broilers. The phylogenetic analysis also revealed that majority of the Malaysian commercial chickens were randomly assembled with the Type-II village chickens. In RAPD assay, phylogenetic analysis using neighbor-joining produced six clusters that were completely distinguished based on the locality of chickens. High levels of genetic variations were observed among the village chickens, the commercial broilers, and between the commercial broilers and layer chickens. In this study, it was found that Type-I village chickens could be distinguished from the commercial chickens and Type-II village chickens at the position of the 27th nucleotide of the 351 bp cytochrome b gene. This study also revealed that RAPD markers were unable to differentiate the type of chickens, but it showed the effectiveness of RAPD in evaluating the genetic variation and the genetic relationships between chicken lines and populations.

Relation between the Dynamics of Glassy Clusters and Characteristic Features of their Energy Landscape

NASA Astrophysics Data System (ADS)

De, Sandip; Schaefer, Bastian; Sadeghi, Ali; Sicher, Michael; Kanhere, D. G.; Goedecker, Stefan

2014-02-01

Based on a recently introduced metric for measuring distances between configurations, we introduce distance-energy (DE) plots to characterize the potential energy surface of clusters. Producing such plots is computationally feasible on the density functional level since it requires only a few hundred stable low energy configurations including the global minimum. By using standard criteria based on disconnectivity graphs and the dynamics of Lennard-Jones clusters, we show that the DE plots convey the necessary information about the character of the potential energy surface and allow us to distinguish between glassy and nonglassy systems. We then apply this analysis to real clusters at the density functional theory level and show that both glassy and nonglassy clusters can be found in simulations. It turns out that among our investigated clusters only those can be synthesized experimentally which exhibit a nonglassy landscape.

A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

PubMed

Sugii, Yuh; Kasai, Tomonari; Ikeda, Masashi; Vaidyanath, Arun; Kumon, Kazuki; Mizutani, Akifumi; Seno, Akimasa; Tokutaka, Heizo; Kudoh, Takayuki; Seno, Masaharu

2016-01-01

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

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.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

Computer-assisted cytologic diagnosis in pancreatic FNA: An application of neural networks to image analysis.

PubMed

Momeni-Boroujeni, Amir; Yousefi, Elham; Somma, Jonathan

2017-12-01

Fine-needle aspiration (FNA) biopsy is an accurate method for the diagnosis of solid pancreatic masses. However, a significant number of cases still pose a diagnostic challenge. The authors have attempted to design a computer model to aid in the diagnosis of these biopsies. Images were captured of cell clusters on ThinPrep slides from 75 pancreatic FNA cases (20 malignant, 24 benign, and 31 atypical). A K-means clustering algorithm was used to segment the cell clusters into separable regions of interest before extracting features similar to those used for cytomorphologic assessment. A multilayer perceptron neural network (MNN) was trained and then tested for its ability to distinguish benign from malignant cases. A total of 277 images of cell clusters were obtained. K-means clustering identified 68,301 possible regions of interest overall. Features such as contour, perimeter, and area were found to be significantly different between malignant and benign images (P <.05). The MNN was 100% accurate for benign and malignant categories. The model's predictions from the atypical data set were 77% accurate. The results of the current study demonstrate that computer models can be used successfully to distinguish benign from malignant pancreatic cytology. The fact that the model can categorize atypical cases into benign or malignant with 77% accuracy highlights the great potential of this technology. Although further study is warranted to validate its clinical applications in pancreatic and perhaps other areas of cytology as well, the potential for improved patient outcomes using MNN for image analysis in pathology is significant. Cancer Cytopathol 2017;125:926-33. © 2017 American Cancer Society. © 2017 American Cancer Society.

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.

An application of bioassessment metrics and multivariate techniques to evaluate central Nebraska streams

USGS Publications Warehouse

Frenzel, S.A.

1996-01-01

Ninety-one stream sites in central Nebraska were classified into four clusters on the basis of a cluster analysis (TWINSPAN) of macroinvertebrate data. Rapid bioassessment protocol scores for macroinvertebrate species were significantly different among sites grouped by teh first division into two clusters. This division may have distinguished sites on the basis of water-quality imparement. Individual metrics that differed between clusters of sites were the Hilsenhoff Biotic Index, the number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, and the ratio of individuals in EPT to Chironomidae taxa. Canonical correspondence analysis of 57 of 91 sites showed that stream width, site altitude, latitude, soil permeability, water temperature, and mean annual precipitation were the most important environmental variables describing variance in the species-environment relation. Stream width and soil permeability reflected streamflow characteristics of a site, whereas site altitude and latitude were factors related to general climatic conditions. Mean annual precipitation related to both streamflow and climatic conditions.

Use of biochemical kinetic data to determine strain relatedness among Salmonella enterica subsp. enterica isolates.

PubMed

de la Torre, E; Tello, M; Mateu, E M; Torre, E

2005-11-01

Classical biotyping characterizes strains by creating biotype profiles that consider only positive and negative results for a predefined set of biochemical tests. This method allows Salmonella subspecies to be distinguished but does not allow serotypes and phage types to be distinguished. The objective of this study was to determine the relatedness of isolates belonging to distinct Salmonella enterica subsp. enterica serotypes by using a refined biotyping process that considers the kinetics at which biochemical reactions take place. Using a Vitek GNI+ card for the identification of gram-negative organisms, we determined the biochemical kinetic reactions (28 biochemical tests) of 135 Salmonella enterica subsp. enterica strains of pig origin collected in Spain from 1997 to 2002 (59 Salmonella serotype Typhimurium strains, 25 Salmonella serotype Typhimurium monophasic variant strains, 25 Salmonella serotype Anatum strains, 12 Salmonella serotype Tilburg strains, 7 Salmonella serotype Virchow strains, 6 Salmonella serotype Choleraesuis strains, and 1 Salmonella enterica serotype 4,5,12:-:- strain). The results were expressed as the colorimetric and turbidimetric changes (in percent) and were used to enhance the classical biotype profile by adding kinetic categories. A hierarchical cluster analysis was performed by using the enhanced profiles and resulted in 14 clusters. Six major clusters grouped 94% of all isolates with a similarity of > or =95% within any given cluster, and eight clusters contained a single isolate. The six major clusters grouped not only serotypes of the same type but also phenotypic serotype variations into individual clusters. This suggests that metabolic kinetic reaction data from the biochemical tests commonly used for classic Salmonella enterica subsp. enterica biotyping can possibly be used to determine the relatedness between isolates in an easy and timely manner.

A novel symptom cluster analysis among ambulatory HIV/AIDS patients in Uganda.

PubMed

Namisango, Eve; Harding, Richard; Katabira, Elly T; Siegert, Richard J; Powell, Richard A; Atuhaire, Leonard; Moens, Katrien; Taylor, Steve

2015-01-01

Symptom clusters are gaining importance given HIV/AIDS patients experience multiple, concurrent symptoms. This study aimed to: determine clusters of patients with similar symptom combinations; describe symptom combinations distinguishing the clusters; and evaluate the clusters regarding patient socio-demographic, disease and treatment characteristics, quality of life (QOL) and functional performance. This was a cross-sectional study of 302 adult HIV/AIDS outpatients consecutively recruited at two teaching and referral hospitals in Uganda. Socio-demographic and seven-day period symptom prevalence and distress data were self-reported using the Memorial Symptom Assessment Schedule. QOL was assessed using the Medical Outcome Scale and functional performance using the Karnofsky Performance Scale. Symptom clusters were established using hierarchical cluster analysis with squared Euclidean distances using Ward's clustering methods based on symptom occurrence. Analysis of variance compared clusters on mean QOL and functional performance scores. Patient subgroups were categorised based on symptom occurrence rates. Five symptom occurrence clusters were identified: Cluster 1 (n=107), high-low for sensory discomfort and eating difficulties symptoms; Cluster 2 (n=47), high-low for psycho-gastrointestinal symptoms; Cluster 3 (n=71), high for pain and sensory disturbance symptoms; Cluster 4 (n=35), all high for general HIV/AIDS symptoms; and Cluster 5 (n=48), all low for mood-cognitive symptoms. The all high occurrence cluster was associated with worst functional status, poorest QOL scores and highest symptom-associated distress. Use of antiretroviral therapy was associated with all high symptom occurrence rate (Fisher's exact=4, P<0.001). CD4 count group below 200 was associated with the all high occurrence rate symptom cluster (Fisher's exact=41, P<0.001). Symptom clusters have a differential, affect HIV/AIDS patients' self-reported outcomes, with the subgroup experiencing high-symptom occurrence rates having a higher risk of poorer outcomes. Identification of symptom clusters could provide insights into commonly co-occurring symptoms that should be jointly targeted for management in patients with multiple complaints.

Pathological and non-pathological variants of restrictive eating behaviors in middle childhood: A latent class analysis.

PubMed

Schmidt, Ricarda; Vogel, Mandy; Hiemisch, Andreas; Kiess, Wieland; Hilbert, Anja

2018-08-01

Although restrictive eating behaviors are very common during early childhood, their precise nature and clinical correlates remain unclear. Especially, there is little evidence on restrictive eating behaviors in older children and their associations with children's shape concern. The present population-based study sought to delineate subgroups of restrictive eating patterns in N = 799 7-14 year old children. Using Latent Class Analysis, children were classified based on six restrictive eating behaviors (for example, picky eating, food neophobia, and eating-related anxiety) and shape concern, separately in three age groups. For cluster validation, sociodemographic and objective anthropometric data, parental feeding practices, and general and eating disorder psychopathology were used. The results showed a 3-cluster solution across all age groups: an asymptomatic class (Cluster 1), a class with restrictive eating behaviors without shape concern (Cluster 2), and a class showing restrictive eating behaviors with prominent shape concern (Cluster 3). The clusters differed in all variables used for validation. Particularly, the proportion of children with symptoms of avoidant/restrictive food intake disorder was greater in Cluster 2 than Clusters 1 and 3. The study underlined the importance of considering shape concern to distinguish between different phenotypes of children's restrictive eating patterns. Longitudinal data are needed to evaluate the clusters' predictive effects on children's growth and development of clinical eating disorders. Copyright © 2018 Elsevier Ltd. All rights reserved.

Choosing the Number of Clusters in K-Means Clustering

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

Coupled-cluster treatment of molecular strong-field ionization

NASA Astrophysics Data System (ADS)

Jagau, Thomas-C.

2018-05-01

Ionization rates and Stark shifts of H2, CO, O2, H2O, and CH4 in static electric fields have been computed with coupled-cluster methods in a basis set of atom-centered Gaussian functions with a complex-scaled exponent. Consideration of electron correlation is found to be of great importance even for a qualitatively correct description of the dependence of ionization rates and Stark shifts on the strength and orientation of the external field. The analysis of the second moments of the molecular charge distribution suggests a simple criterion for distinguishing tunnel and barrier suppression ionization in polyatomic molecules.

Rapid identification of Enterobacter hormaechei and Enterobacter cloacae genetic cluster III.

PubMed

Ohad, S; Block, C; Kravitz, V; Farber, A; Pilo, S; Breuer, R; Rorman, E

2014-05-01

Enterobacter cloacae complex bacteria are of both clinical and environmental importance. Phenotypic methods are unable to distinguish between some of the species in this complex, which often renders their identification incomplete. The goal of this study was to develop molecular assays to identify Enterobacter hormaechei and Ent. cloacae genetic cluster III which are relatively frequently encountered in clinical material. The molecular assays developed in this study are qPCR technology based and served to identify both Ent. hormaechei and Ent. cloacae genetic cluster III. qPCR results were compared to hsp60 sequence analysis. Most clinical isolates were assigned to Ent. hormaechei subsp. steigerwaltii and Ent. cloacae genetic cluster III. The latter was proportionately more frequently isolated from bloodstream infections than from other material (P < 0·05). The qPCR assays detecting Ent. hormaechei and Ent. cloacae genetic cluster III demonstrated high sensitivity and specificity. The presented qPCR assays allow accurate and rapid identification of clinical isolates of the Ent. cloacae complex. The improved identifications obtained can specifically assist analysis of Ent. hormaechei and Ent. cloacae genetic cluster III in nosocomial outbreaks and can promote rapid environmental monitoring. An association was observed between Ent. cloacae cluster III and systemic infection that deserves further attention. © 2014 The Society for Applied Microbiology.

Discriminating Characteristics of Tectonic and Human-Induced Seismicity

NASA Astrophysics Data System (ADS)

Zaliapin, I. V.; Ben-Zion, Y.

2015-12-01

We analyze statistical features of background and clustered subpopulations of earthquakes in different regions in an effort to distinguish between human-induced and natural seismicity. Analysis of "end-member" areas known to be dominated by human-induced earthquakes (the Geyser geothermal field in northern California and TauTona gold mine in South Africa) and regular tectonic activity (the San Jacinto fault zone in southern California and Coso region excluding the Coso geothermal field in eastern central California) reveals several distinguishing characteristics. Induced seismicity is shown to have (i) higher rate of background events (both absolute and relative to the total rate), (ii) faster temporal offspring decay, (iii) higher intensity of repeating events, (iv) larger proportion of small clusters, and (v) larger spatial separation between parent and offspring, compared to regular tectonic activity. These differences also successfully discriminate seismicity within the Coso and Salton Sea geothermal fields in California before and after the expansion of geothermal production during the 1980s.

Cognitive subtypes of dyslexia are characterized by distinct patterns of grey matter volume.

PubMed

Jednoróg, Katarzyna; Gawron, Natalia; Marchewka, Artur; Heim, Stefan; Grabowska, Anna

2014-09-01

The variety of different causal theories together with inconsistencies about the anatomical brain markers emphasize the heterogeneity of developmental dyslexia. Attempts were made to test on a behavioral level the existence of subtypes of dyslexia showing distinguishable cognitive deficits. Importantly, no research was directly devoted to the investigation of structural brain correlates of these subtypes. Here, for the first time, we applied voxel-based morphometry (VBM) to study grey matter volume (GMV) differences in a relatively large sample (n = 46) of dyslexic children split into three subtypes based on the cognitive deficits: phonological, rapid naming, magnocellular/dorsal, and auditory attention shifting. VBM revealed GMV clusters specific for each studied group including areas of left inferior frontal gyrus, cerebellum, right putamen, and bilateral parietal cortex. In addition, using discriminant analysis on these clusters 79% of cross-validated cases were correctly re-classified into four groups (controls vs. three subtypes). Current results indicate that dyslexia may result from distinct cognitive impairments characterized by distinguishable anatomical markers.

X-Ray Morphological Analysis of the Planck ESZ Clusters

NASA Astrophysics Data System (ADS)

Lovisari, Lorenzo; Forman, William R.; Jones, Christine; Ettori, Stefano; Andrade-Santos, Felipe; Arnaud, Monique; Démoclès, Jessica; Pratt, Gabriel W.; Randall, Scott; Kraft, Ralph

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev-Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper we determine eight morphological parameters for the Planck Early Sunyaev-Zeldovich (ESZ) objects observed with XMM-Newton. We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.

X-Ray Morphological Analysis of the Planck ESZ Clusters

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

Lovisari, Lorenzo; Forman, William R.; Jones, Christine

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev–Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper wemore » determine eight morphological parameters for the Planck Early Sunyaev–Zeldovich (ESZ) objects observed with XMM-Newton . We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.« less

Analytics For Distracted Driver Behavior Modeling in Dilemma Zone

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

Li, Jan-Mou; Malikopoulos, Andreas; Thakur, Gautam

2014-01-01

In this paper, we present the results obtained and insights gained through the analysis of TRB contest data. We used exploratory analysis, regression, and clustering models for gaining insights into the driver behavior in a dilemma zone while driving under distraction. While simple exploratory analysis showed the distinguishing driver behavior patterns among different popu- lation groups in the dilemma zone, regression analysis showed statically signification relationships between groups of variables. In addition to analyzing the contest data, we have also looked into the possible impact of distracted driving on the fuel economy.

A Cluster Analysis of Bronchial Asthma Patients with Depressive Symptoms.

PubMed

Seino, Yo; Hasegawa, Takashi; Koya, Toshiyuki; Sakagami, Takuro; Mashima, Ichiro; Shimizu, Natsue; Muramatsu, Yoshiyuki; Muramatsu, Kumiko; Suzuki, Eiichi; Kikuchi, Toshiaki

2018-03-09

Objective Whether or not depression affects the control or severity of asthma is unclear. We performed a cluster analysis of asthma patients with depressive symptoms to clarify their characteristics. Methods and subjects Multiple medical institutions in Niigata Prefecture, Japan, were surveyed in 2014. We recorded the age, disease duration, body mass index (BMI), medications, and surveyed asthma control status and severity, as well as depressive symptoms and adherence to treatment using questionnaires. A hierarchical cluster analysis was performed on the group of patients assessed as having depression. Results Of 2,273 patients, 128 were assessed as being positive for depressive symptoms (DS[+]). Thirty-three were excluded because of missing data, and the remaining 95 DS[+] patients were classified into 3 clusters (A, B, and C). The patients in cluster A (n=19) were elderly, had severe, poorly controlled asthma, and demonstrated possible adherence barriers; those in cluster B (n=26) were elderly with a low BMI and had no significant adherence barriers but had severe, poorly controlled asthma; and those in cluster C (n=50) were younger, with a high BMI, no significant adherence barriers, well-controlled asthma, and few were severely affected. The scores for depressive symptoms were not significantly different between clusters. Conclusion About half of the patients in the DS[+] group had severe, poorly controlled asthma, and these clusters were able to be distinguished by their ASK-12 score, which reflects adherence barriers. The control status and severity of asthma may also be related to the age, disease duration, and BMI in the DS[+] group.

Exploring the individual patterns of spiritual well-being in people newly diagnosed with advanced cancer: a cluster analysis.

PubMed

Bai, Mei; Dixon, Jane; Williams, Anna-Leila; Jeon, Sangchoon; Lazenby, Mark; McCorkle, Ruth

2016-11-01

Research shows that spiritual well-being correlates positively with quality of life (QOL) for people with cancer, whereas contradictory findings are frequently reported with respect to the differentiated associations between dimensions of spiritual well-being, namely peace, meaning and faith, and QOL. This study aimed to examine individual patterns of spiritual well-being among patients newly diagnosed with advanced cancer. Cluster analysis was based on the twelve items of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale at Time 1. A combination of hierarchical and k-means (non-hierarchical) clustering methods was employed to jointly determine the number of clusters. Self-rated health, depressive symptoms, peace, meaning and faith, and overall QOL were compared at Time 1 and Time 2. Hierarchical and k-means clustering methods both suggested four clusters. Comparison of the four clusters supported statistically significant and clinically meaningful differences in QOL outcomes among clusters while revealing contrasting relations of faith with QOL. Cluster 1, Cluster 3, and Cluster 4 represented high, medium, and low levels of overall QOL, respectively, with correspondingly high, medium, and low levels of peace, meaning, and faith. Cluster 2 was distinguished from other clusters by its medium levels of overall QOL, peace, and meaning and low level of faith. This study provides empirical support for individual difference in response to a newly diagnosed cancer and brings into focus conceptual and methodological challenges associated with the measure of spiritual well-being, which may partly contribute to the attenuated relation between faith and QOL.

Walking Stroop carpet: an innovative dual-task concept for detecting cognitive impairment.

PubMed

Perrochon, A; Kemoun, G; Watelain, E; Berthoz, A

2013-01-01

Several studies have reported the potential value of the dual-task concept during locomotion in clinical evaluation because cognitive decline is strongly associated with gait abnormalities. However, current dual-task tests appear to be insufficient for early diagnosis of cognitive impairment. Forty-nine subjects (young, old, with or without mild cognitive impairment) underwent cognitive evaluation (Mini-Mental State Examination, Frontal Assessment Battery, five-word test, Stroop, clock-drawing) and single-task locomotor evaluation on an electronic walkway. They were then dual-task-tested on the Walking Stroop carpet, which is an adaptation of the Stroop color-word task for locomotion. A cluster analysis, followed by an analysis of variance, was performed to assess gait parameters. Cluster analysis of gait parameters on the Walking Stroop carpet revealed an interaction between cognitive and functional abilities because it made it possible to distinguish dysexecutive cognitive fragility or decline with a sensitivity of 89% and a specificity of 94%. Locomotor abilities differed according to the group and dual-task conditions. Healthy subjects performed less well on dual-tasking under reading conditions than when they were asked to distinguish colors, whereas dysexecutive subjects had worse motor performances when they were required to dual task. The Walking Stroop carpet is a dual-task test that enables early detection of cognitive fragility that has not been revealed by traditional neuropsychological tests or single-task walking analysis.

Walking Stroop carpet: an innovative dual-task concept for detecting cognitive impairment

PubMed Central

Perrochon, A; Kemoun, G; Watelain, E; Berthoz, A

2013-01-01

Background Several studies have reported the potential value of the dual-task concept during locomotion in clinical evaluation because cognitive decline is strongly associated with gait abnormalities. However, current dual-task tests appear to be insufficient for early diagnosis of cognitive impairment. Methods Forty-nine subjects (young, old, with or without mild cognitive impairment) underwent cognitive evaluation (Mini-Mental State Examination, Frontal Assessment Battery, five-word test, Stroop, clock-drawing) and single-task locomotor evaluation on an electronic walkway. They were then dual-task-tested on the Walking Stroop carpet, which is an adaptation of the Stroop color–word task for locomotion. A cluster analysis, followed by an analysis of variance, was performed to assess gait parameters. Results Cluster analysis of gait parameters on the Walking Stroop carpet revealed an interaction between cognitive and functional abilities because it made it possible to distinguish dysexecutive cognitive fragility or decline with a sensitivity of 89% and a specificity of 94%. Locomotor abilities differed according to the group and dual-task conditions. Healthy subjects performed less well on dual-tasking under reading conditions than when they were asked to distinguish colors, whereas dysexecutive subjects had worse motor performances when they were required to dual task. Conclusion The Walking Stroop carpet is a dual-task test that enables early detection of cognitive fragility that has not been revealed by traditional neuropsychological tests or single-task walking analysis. PMID:23682211

Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.

Cross-correlating the γ-ray Sky with Catalogs of Galaxy Clusters

NASA Astrophysics Data System (ADS)

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro; Fornengo, Nicolao; Regis, Marco; Viel, Matteo; Xia, Jun-Qing

2017-01-01

We report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few to tens of megaparsecs, I.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, I.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. We argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.

Subtyping depression by clinical features: the Australasian database.

PubMed

Parker, G; Roy, K; Hadzi-Pavlovic, D; Mitchell, P; Wilhelm, K; Menkes, D B; Snowdon, J; Loo, C; Schweitzer, I

2000-01-01

To distinguish psychotic, melancholic and a residual non-melancholic class on the basis of clinical features alone. Previous studies at our Mood Disorders Unit (MDU) favour a hierarchical model, with the classes able to be distinguished by two specific clinical features, but any such intramural study risks rater bias and requires external replication. This replication study involved 27 Australasian psychiatrist raters, thus extending the sample and raters beyond the MDU facility. They collected clinical feature data using a standardized assessment with precoded rating options. A psychotic depression (PD) class was derived by respecting DSM-IV decision rules while a cluster analysis distinguished melancholic (MEL) and non-melancholic classes. The MELs were distinguished virtually entirely by the presence of significant psychomotor disturbance (PMD), as rated by the observationally based CORE measure, with over-representation on only three of an extensive set of 'endogeneity symptoms'. In comparison to PMD, endogeneity symptoms appear to be poor indicators of 'melancholic' type, confounding typology with severity. Results again support the hierarchical model.

Consanguinity and family clustering of male factor infertility in Lebanon.

PubMed

Inhorn, Marcia C; Kobeissi, Loulou; Nassar, Zaher; Lakkis, Da'ad; Fakih, Michael H

2009-04-01

To investigate the influence of consanguineous marriage on male factor infertility in Lebanon, where rates of consanguineous marriage remain high (29.6% among Muslims, 16.5% among Christians). Clinic-based, case-control study, using reproductive history, risk factor interview, and laboratory-based semen analysis. Two IVF clinics in Beirut, Lebanon, during an 8-month period (January-August 2003). One hundred twenty infertile male patients and 100 fertile male controls, distinguished by semen analysis and reproductive history. None. Standard clinical semen analysis. The rates of consanguineous marriage were relatively high among the study sample. Patients (46%) were more likely than controls (37%) to report first-degree (parental) and second-degree (grandparental) consanguinity. The study demonstrated a clear pattern of family clustering of male factor infertility, with patients significantly more likely than controls to report infertility among close male relatives (odds ratio = 2.58). Men with azoospermia and severe oligospermia showed high rates of both consanguinity (50%) and family clustering (41%). Consanguineous marriage is a socially supported institution throughout the Muslim world, yet its relationship to infertility is poorly understood. This study demonstrated a significant association between consanguinity and family clustering of male factor infertility cases, suggesting a strong genetic component.

Cluster analysis of stress corrosion mechanisms for steel wires used in bridge cables through acoustic emission particle swarm optimization.

PubMed

Li, Dongsheng; Yang, Wei; Zhang, Wenyao

2017-05-01

Stress corrosion is the major failure type of bridge cable damage. The acoustic emission (AE) technique was applied to monitor the stress corrosion process of steel wires used in bridge cable structures. The damage evolution of stress corrosion in bridge cables was obtained according to the AE characteristic parameter figure. A particle swarm optimization cluster method was developed to determine the relationship between the AE signal and stress corrosion mechanisms. Results indicate that the main AE sources of stress corrosion in bridge cables included four types: passive film breakdown and detachment of the corrosion product, crack initiation, crack extension, and cable fracture. By analyzing different types of clustering data, the mean value of each damage pattern's AE characteristic parameters was determined. Different corrosion damage source AE waveforms and the peak frequency were extracted. AE particle swarm optimization cluster analysis based on principal component analysis was also proposed. This method can completely distinguish the four types of damage sources and simplifies the determination of the evolution process of corrosion damage and broken wire signals. Copyright © 2017. Published by Elsevier B.V.

Identification of cephalopod species from the North and Baltic Seas using morphology, COI and 18S rDNA sequences

NASA Astrophysics Data System (ADS)

Gebhardt, Katharina; Knebelsberger, Thomas

2015-09-01

We morphologically analyzed 79 cephalopod specimens from the North and Baltic Seas belonging to 13 separate species. Another 29 specimens showed morphological features of either Alloteuthis mediaor Alloteuthis subulata or were found to be in between. Reliable identification features to distinguish between A. media and A. subulata are currently not available. The analysis of the DNA barcoding region of the COI gene revealed intraspecific distances (uncorrected p) ranging from 0 to 2.13 % (average 0.1 %) and interspecific distances between 3.31 and 22 % (average 15.52 %). All species formed monophyletic clusters in a neighbor-joining analysis and were supported by bootstrap values of ≥99 %. All COI haplotypes belonging to the 29 Alloteuthis specimens were grouped in one cluster. Neither COI nor 18S rDNA sequences helped to distinguish between the different Alloteuthis morphotypes. For species identification purposes, we recommend the use of COI, as it showed higher bootstrap support of species clusters and less amplification and sequencing failure compared to 18S. Our data strongly support the assumption that the genus Alloteuthis is only represented by a single species, at least in the North Sea. It remained unclear whether this species is A. subulata or A. media. All COI sequences including important metadata were uploaded to the Barcode of Life Data Systems and can be used as reference library for the molecular identification of more than 50 % of the cephalopod fauna known from the North and Baltic Seas.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

NASA Astrophysics Data System (ADS)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

GazeAppraise v. 0.1

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

Wilson, Andrew; Haass, Michael; Rintoul, Mark Daniel

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths.more » An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.« less

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

Multi-party Measurement-Device-Independent Quantum Key Distribution Based on Cluster States

NASA Astrophysics Data System (ADS)

Liu, Chuanqi; Zhu, Changhua; Ma, Shuquan; Pei, Changxing

2018-03-01

We propose a novel multi-party measurement-device-independent quantum key distribution (MDI-QKD) protocol based on cluster states. A four-photon analyzer which can distinguish all the 16 cluster states serves as the measurement device for four-party MDI-QKD. Any two out of four participants can build secure keys after the analyzers obtains successful outputs and the two participants perform post-processing. We derive a security analysis for the protocol, and analyze the key rates under different values of polarization misalignment. The results show that four-party MDI-QKD is feasible over 280 km in the optical fiber channel when the key rate is about 10- 6 with the polarization misalignment parameter 0.015. Moreover, our work takes an important step toward a quantum communication network.

A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

PubMed Central

Sawacha, Zimi; Guarneri, Gabriella; Avogaro, Angelo; Cobelli, Claudio

2010-01-01

Background The diabetic foot, one of the most serious complications of diabetes mellitus and a major risk factor for plantar ulceration, is determined mainly by peripheral neuropathy. Neuropathic patients exhibit decreased stability while standing as well as during dynamic conditions. A new methodology for diabetic gait pattern classification based on cluster analysis has been proposed that aims to identify groups of subjects with similar patterns of gait and verify if three-dimensional gait data are able to distinguish diabetic gait patterns from one of the control subjects. Method The gait of 20 nondiabetic individuals and 46 diabetes patients with and without peripheral neuropathy was analyzed [mean age 59.0 (2.9) and 61.1(4.4) years, mean body mass index (BMI) 24.0 (2.8), and 26.3 (2.0)]. K-means cluster analysis was applied to classify the subjects' gait patterns through the analysis of their ground reaction forces, joints and segments (trunk, hip, knee, ankle) angles, and moments. Results Cluster analysis classification led to definition of four well-separated clusters: one aggregating just neuropathic subjects, one aggregating both neuropathics and non-neuropathics, one including only diabetes patients, and one including either controls or diabetic and neuropathic subjects. Conclusions Cluster analysis was useful in grouping subjects with similar gait patterns and provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented for any specific variable. In particular, we observed the presence of neuropathic subjects with a gait similar to the controls and diabetes patients with a long disease duration with a gait as altered as the neuropathic one. PMID:20920432

An approach to functionally relevant clustering of the protein universe: Active site profile‐based clustering of protein structures and sequences

PubMed Central

Knutson, Stacy T.; Westwood, Brian M.; Leuthaeuser, Janelle B.; Turner, Brandon E.; Nguyendac, Don; Shea, Gabrielle; Kumar, Kiran; Hayden, Julia D.; Harper, Angela F.; Brown, Shoshana D.; Morris, John H.; Ferrin, Thomas E.; Babbitt, Patricia C.

2017-01-01

Abstract Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification—amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two‐Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure‐Function Linkage Database, SFLD) self‐identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self‐identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well‐curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP‐identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F‐measure and performance analysis on the enolase search results and comparison to GEMMA and SCI‐PHY demonstrate that TuLIP avoids the over‐division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results. PMID:28054422

An approach to functionally relevant clustering of the protein universe: Active site profile-based clustering of protein structures and sequences.

PubMed

Knutson, Stacy T; Westwood, Brian M; Leuthaeuser, Janelle B; Turner, Brandon E; Nguyendac, Don; Shea, Gabrielle; Kumar, Kiran; Hayden, Julia D; Harper, Angela F; Brown, Shoshana D; Morris, John H; Ferrin, Thomas E; Babbitt, Patricia C; Fetrow, Jacquelyn S

2017-04-01

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results. © 2017 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Techniques for investigation of an apparent outbreak of infections with Candida glabrata.

PubMed Central

Arif, S; Barkham, T; Power, E G; Howell, S A

1996-01-01

A cluster of Candida glabrata isolates recovered from seven patients in an intensive care unit over a 10-week period were compared with a collection of isolates from six epidemiologically distinct outpatients and a reference strain by several DNA typing methods. Restriction enzyme analysis with HinII distinguished 13 strains from the 14 sources and was the method of choice. Pulsed-field gel electrophoresis and random amplification of polymorphic DNA both detected nine types from the 14 sources; however, the results of these two methods did not always correlate. These methods demonstrated that five of the seven patients had distinguishable strains and that cross-infection was unlikely. PMID:8862586

Environmental factors influence on chemical polymorphism of the essential oils of Lychnophora ericoides.

PubMed

Curado, Marco A; Oliveira, Carolina B A; Jesus, José G; Santos, Suzana C; Seraphin, José C; Ferri, Pedro H

2006-11-01

Lychnophora ericoides is a Brazilian medicinal plant used in folk medicine as an anti-inflammatory and analgesic agent. The essential oils from leaves of two populations with and without scent, collected at 2-month intervals during an 1-year period, were analysed by GC-MS. The results were submitted to principal component and cluster analysis which allowed two groups of essential oils to be distinguished with respect to sampling site and scent: cluster I (Vianópolis site, with specimens exhibiting an aromatic scent) containing a high percentage of alpha-bisabolol (44.7-76.4%) and alpha-cadinol (10.9-23.5%), and cluster II (Cristalina site, with specimens without scent) characterised by a high content of (E)-nerolidol (31.3-47.1%) and ar-dihydro-turmerone (4.8-15.4%). The canonical discriminant analysis showed that using the data set of the seven sampling months and (E)-nerolidol and alpha-bisabolol as predictable variables, it was possible to distinguish between the samples harvested according to Cerrado seasons, dry winter (May-September) and humid summer (November-March). In addition, canonical correlation analysis between the soil sampling sites and the populations revealed a significant relationship between oil components and edaphic factors. Oxygenated sesquiterpenes and potential acidity, Al saturation, cationic exchange capacity, silt, and sand load as the first canonical variate were fairly strongly related to samples collected in Vianópolis site. On the other hand, monoterpenes and sesquiterpene hydrocarbons were strongly related to chemical balance in soils (organic matter, P and base saturation), which is related to samples at the Cristalina site. The chemovariation observed appears to be environmentally determined.

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2017-06-01

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

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

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro

In this article, we report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few tomore » tens of megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. Lastly, we argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

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

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro

We report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ -ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few to tens ofmore » megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ -ray emission from the intracluster medium. We argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

CROSS-CORRELATING THE γ-RAY SKY WITH CATALOGS OF GALAXY CLUSTERS

DOE PAGES

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro; ...

2017-01-18

In this article, we report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few tomore » tens of megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. Lastly, we argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

Meta-Analytical Online Repository of Gene Expression Profiles of MDS Stem Cells

DTIC Science & Technology

2015-12-01

Myelodysplastic syndrome , AML: Acute myeloid leukemia, ALL: Acute lymphoblastic leukemia Numbers in brackets are reference numbers. doi:10.1371/journal.pone...disorders such as acute leukemias and myelodysplastic syndromes would be distinguishable in our analysis. Unsupervised clustering showed that even though...al. Angiogenesis in acute and chronic leukemias and myelodysplastic syndromes . Blood. 2000;96:2240–2245. [PubMed: 10979972] 17. Yoon SY, Li CY, Lloyd

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

PubMed Central

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-01-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required. PMID:26156000

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

NASA Astrophysics Data System (ADS)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-07-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

CANTAB object recognition and language tests to detect aging cognitive decline: an exploratory comparative study

PubMed Central

Cabral Soares, Fernanda; de Oliveira, Thaís Cristina Galdino; de Macedo, Liliane Dias e Dias; Tomás, Alessandra Mendonça; Picanço-Diniz, Domingos Luiz Wanderley; Bento-Torres, João; Bento-Torres, Natáli Valim Oliver; Picanço-Diniz, Cristovam Wanderley

2015-01-01

Objective The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects. Method We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group. Results Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects. Conclusion Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging. PMID:25565785

Towards the use of computationally inserted lesions for mammographic CAD assessment

NASA Astrophysics Data System (ADS)

Ghanian, Zahra; Pezeshk, Aria; Petrick, Nicholas; Sahiner, Berkman

2018-03-01

Computer-aided detection (CADe) devices used for breast cancer detection on mammograms are typically first developed and assessed for a specific "original" acquisition system, e.g., a specific image detector. When CADe developers are ready to apply their CADe device to a new mammographic acquisition system, they typically assess the CADe device with images acquired using the new system. Collecting large repositories of clinical images containing verified cancer locations and acquired by the new image acquisition system is costly and time consuming. Our goal is to develop a methodology to reduce the clinical data burden in the assessment of a CADe device for use with a different image acquisition system. We are developing an image blending technique that allows users to seamlessly insert lesions imaged using an original acquisition system into normal images or regions acquired with a new system. In this study, we investigated the insertion of microcalcification clusters imaged using an original acquisition system into normal images acquired with that same system utilizing our previously-developed image blending technique. We first performed a reader study to assess whether experienced observers could distinguish between computationally inserted and native clusters. For this purpose, we applied our insertion technique to clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM) and the Breast Cancer Digital Repository (BCDR). Regions of interest containing microcalcification clusters from one breast of a patient were inserted into the contralateral breast of the same patient. The reader study included 55 native clusters and their 55 inserted counterparts. Analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicated that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve, AUC=0.58±0.04). Furthermore, CADe sensitivity was evaluated on mammograms with native and inserted microcalcification clusters using a commercial CADe system. For this purpose, we used full field digital mammograms (FFDMs) from 68 clinical cases, acquired at the University of Michigan Health System. The average sensitivities for native and inserted clusters were equal, 85.3% (58/68). These results demonstrate the feasibility of using the inserted microcalcification clusters for assessing mammographic CAD devices.

Cluster Analysis of Velocity Field Derived from Dense GNSS Network of Japan

NASA Astrophysics Data System (ADS)

Takahashi, A.; Hashimoto, M.

2015-12-01

Dense GNSS networks have been widely used to observe crustal deformation. Simpson et al. (2012) and Savage and Simpson (2013) have conducted cluster analyses of GNSS velocity field in the San Francisco Bay Area and Mojave Desert, respectively. They have successfully found velocity discontinuities. They also showed an advantage of cluster analysis for classifying GNSS velocity field. Since in western United States, strike-slip events are dominant, geometry is simple. However, the Japanese Islands are tectonically complicated due to subduction of oceanic plates. There are many types of crustal deformation such as slow slip event and large postseismic deformation. We propose a modified clustering method of GNSS velocity field in Japan to separate time variant and static crustal deformation. Our modification is performing cluster analysis every several months or years, then qualifying cluster member similarity. If a GNSS station moved differently from its neighboring GNSS stations, the station will not belong to in the cluster which includes its surrounding stations. With this method, time variant phenomena were distinguished. We applied our method to GNSS data of Japan from 1996 to 2015. According to the analyses, following conclusions were derived. The first is the clusters boundaries are consistent with known active faults. For examples, the Arima-Takatsuki-Hanaore fault system and the Shimane-Tottori segment proposed by Nishimura (2015) are recognized, though without using prior information. The second is improving detectability of time variable phenomena, such as a slow slip event in northern part of Hokkaido region detected by Ohzono et al. (2015). The last one is the classification of postseismic deformation caused by large earthquakes. The result suggested velocity discontinuities in postseismic deformation of the Tohoku-oki earthquake. This result implies that postseismic deformation is not continuously decaying proportional to distance from its epicenter.

Improving the distinguishable cluster results: spin-component scaling

NASA Astrophysics Data System (ADS)

Kats, Daniel

2018-06-01

The spin-component scaling is employed in the energy evaluation to improve the distinguishable cluster approach. SCS-DCSD reaction energies reproduce reference values with a root-mean-squared deviation well below 1 kcal/mol, the interaction energies are three to five times more accurate than DCSD, and molecular systems with a large amount of static electron correlation are still described reasonably well. SCS-DCSD represents a pragmatic approach to achieve chemical accuracy with a simple method without triples, which can also be applied to multi-configurational molecular systems.

Permanent tooth mineralization in bonobos (Pan paniscus) and chimpanzees (P. troglodytes).

PubMed

Boughner, Julia C; Dean, M Christopher; Wilgenbusch, Chelsea S

2012-12-01

The timing of tooth mineralization in bonobos (Pan paniscus) is virtually uncharacterized. Analysis of these developmental features in bonobos and the possible differences with its sister species, the chimpanzee (P. troglodytes), is important to properly quantify the normal ranges of dental growth variation in closely related primate species. Understanding this variation among bonobo, chimpanzee and modern human dental development is necessary to better contextualize the life histories of extinct hominins. This study tests whether bonobos and chimpanzees are distinguished from each other by covariance among the relative timing and sequences of tooth crown initiation, mineralization, root extension, and completion. Using multivariate statistical analyses, we compared the relative timing of permanent tooth crypt formation, crown mineralization, and root extension between 34 P. paniscus and 80 P. troglodytes mandibles radiographed in lateral and occlusal views. Covariance among our 12 assigned dental scores failed to statistically distinguish between bonobos and chimpanzees. Rather than clustering by species, individuals clustered by age group (infant, younger or older juvenile, and adult). Dental scores covaried similarly between the incisors, as well as between both premolars. Conversely, covariance among dental scores distinguished the canine and each of the three molars not only from each other, but also from the rest of the anterior teeth. Our study showed no significant differences in the relative timing of permanent tooth crown and root formation between bonobos and chimpanzees. Copyright © 2012 Wiley Periodicals, Inc.

[Genetic polymorphism of Tulipa gesneriana L. evaluated on the basis of the ISSR marking data].

PubMed

Kashin, A S; Kritskaya, T A; Schanzer, I A

2016-10-01

Using the method of ISSR analysis, the genetic diversity of 18 natural populations of Tulipa gesneriana L. from the north of the Lower Volga region was examined. The ten ISSR primers used in the study provided identification of 102 PCR fragments, of which 50 were polymorphic (49.0%). According to the proportion of polymorphic markers, two population groups were distinguished: (1) the populations in which the proportion of polymorphic markers ranged from 0.35 to 0.41; (2) the populations in which the proportion of polymorphic markers ranged from 0.64 to 0.85. UPGMA clustering analysis provided subdivision of the sample into two large clusters. The unrooted tree constructed using the Neighbor Joining algorithm had similar topology. The first cluster included slightly variable populations and the second cluster included highly variable populations. The AMOVA analysis showed statistically significant differences (F CT = 0.430; p = 0.000) between the two groups. Local populations are considerably genetically differentiated from each other (F ST = 0.632) and have almost no links via modern gene flow, as evidenced by the results of the Mantel test (r =–0.118; p = 0.819). It is suggested that the degree of genetic similarities and differences between the populations depends on the time and the species dispersal patterns on these territories.

Discrimination of multilocus sequence typing-based Campylobacter jejuni subgroups by MALDI-TOF mass spectrometry.

PubMed

Zautner, Andreas Erich; Masanta, Wycliffe Omurwa; Tareen, Abdul Malik; Weig, Michael; Lugert, Raimond; Groß, Uwe; Bader, Oliver

2013-11-07

Campylobacter jejuni, the most common bacterial pathogen causing gastroenteritis, shows a wide genetic diversity. Previously, we demonstrated by the combination of multi locus sequence typing (MLST)-based UPGMA-clustering and analysis of 16 genetic markers that twelve different C. jejuni subgroups can be distinguished. Among these are two prominent subgroups. The first subgroup contains the majority of hyperinvasive strains and is characterized by a dimeric form of the chemotaxis-receptor Tlp7(m+c). The second has an extended amino acid metabolism and is characterized by the presence of a periplasmic asparaginase (ansB) and gamma-glutamyl-transpeptidase (ggt). Phyloproteomic principal component analysis (PCA) hierarchical clustering of MALDI-TOF based intact cell mass spectrometry (ICMS) spectra was able to group particular C. jejuni subgroups of phylogenetic related isolates in distinct clusters. Especially the aforementioned Tlp7(m+c)(+) and ansB+/ ggt+ subgroups could be discriminated by PCA. Overlay of ICMS spectra of all isolates led to the identification of characteristic biomarker ions for these specific C. jejuni subgroups. Thus, mass peak shifts can be used to identify the C. jejuni subgroup with an extended amino acid metabolism. Although the PCA hierarchical clustering of ICMS-spectra groups the tested isolates into a different order as compared to MLST-based UPGMA-clustering, the isolates of the indicator-groups form predominantly coherent clusters. These clusters reflect phenotypic aspects better than phylogenetic clustering, indicating that the genes corresponding to the biomarker ions are phylogenetically coupled to the tested marker genes. Thus, PCA clustering could be an additional tool for analyzing the relatedness of bacterial isolates.

Computational identification of developmental enhancers:conservation and function of transcription factor binding-site clustersin drosophila melanogaster and drosophila psedoobscura

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

Berman, Benjamin P.; Pfeiffer, Barret D.; Laverty, Todd R.

2004-08-06

The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene, and assayedmore » embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. Measuring conservation of sequence features closely linked to function--such as binding-site clustering--makes better use of comparative sequence data than commonly used methods that examine only sequence identity.« less

Meteorology-induced variations in the spatial behavior of summer ozone pollution in Central California

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

Jin, Ling; Harley, Robert A.; Brown, Nancy J.

Cluster analysis was applied to daily 8 h ozone maxima modeled for a summer season to characterize meteorology-induced variations in the spatial distribution of ozone. Principal component analysis is employed to form a reduced dimension set to describe and interpret ozone spatial patterns. The first three principal components (PCs) capture {approx}85% of total variance, with PC1 describing a general spatial trend, and PC2 and PC3 each describing a spatial contrast. Six clusters were identified for California's San Joaquin Valley (SJV) with two low, three moderate, and one high-ozone cluster. The moderate ozone clusters are distinguished by elevated ozone levels inmore » different parts of the valley: northern, western, and eastern, respectively. The SJV ozone clusters have stronger coupling with the San Francisco Bay area (SFB) than with the Sacramento Valley (SV). Variations in ozone spatial distributions induced by anthropogenic emission changes are small relative to the overall variations in ozone amomalies observed for the whole summer. Ozone regimes identified here are mostly determined by the direct and indirect meteorological effects. Existing measurement sites are sufficiently representative to capture ozone spatial patterns in the SFB and SV, but the western side of the SJV is under-sampled.« less

Phenotypes of sleeplessness: stressing the need for psychodiagnostics in the assessment of insomnia.

PubMed

van de Laar, Merijn; Leufkens, Tim; Bakker, Bart; Pevernagie, Dirk; Overeem, Sebastiaan

2017-09-01

Insomnia is a too general term for various subtypes that might have different etiologies and therefore require different types of treatment. In this explorative study we used cluster analysis to distinguish different phenotypes in 218 patients with insomnia, taking into account several factors including sleep variables and characteristics related to personality and psychiatric comorbidity. Three clusters emerged from the analysis. The 'moderate insomnia with low psychopathology'-cluster was characterized by relatively normal personality traits, as well as normal levels of anxiety and depressive symptoms in the presence of moderate insomnia severity. The 'severe insomnia with moderate psychopathology'-cluster showed relatively high scores on the Insomnia Severity Index and scores on the sleep log that were indicative for severe insomnia. Anxiety and depressive symptoms were slightly above the cut-off and they were characterized by below average self-sufficiency and less goal-directed behavior. The 'early onset insomnia with high psychopathology'-cluster showed a much younger age and earlier insomnia onset than the other two groups. Anxiety and depressive symptoms were well above the cut-off score and the group consisted of a higher percentage of subjects with comorbid psychiatric disorders. This cluster showed a 'typical psychiatric' personality profile. Our findings stress the need for psychodiagnostic procedures next to a sleep-related diagnostic approach, especially in the younger insomnia patients. Specific treatment suggestions are given based on the three phenotypes.

Distinguishing fiction from non-fiction with complex networks

NASA Astrophysics Data System (ADS)

Larue, David M.; Carr, Lincoln D.; Jones, Linnea K.; Stevanak, Joe T.

2014-03-01

Complex Network Measures are applied to networks constructed from texts in English to demonstrate an initial viability in textual analysis. Texts from novels and short stories obtained from Project Gutenberg and news stories obtained from NPR are selected. Unique word stems in a text are used as nodes in an associated unweighted undirected network, with edges connecting words occurring within a certain number of words somewhere in the text. Various combinations of complex network measures are computed for each text's network. Fisher's Linear Discriminant analysis is used to build a parameter optimizing the ability to separate the texts according to their genre. Success rates in the 70% range for correctly distinguishing fiction from non-fiction were obtained using edges defined as within four words, using 400 word samples from 400 texts from each of the two genres with some combinations of measures such as the power-law exponents of degree distributions and clustering coefficients.

Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.

PubMed

Gu, Xun; Ruan, Hang; Su, Zhixi; Zou, Yangyun

2017-09-01

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level. Copyright © 2017. Published by Elsevier Inc.

A New Method Using Single-Particle Mass Spectrometry Data to Distinguish Mineral Dust and Biological Aerosols

NASA Astrophysics Data System (ADS)

Al-Mashat, H.; Kristensen, L.; Sultana, C. M.; Prather, K. A.

2016-12-01

The ability to distinguish types of particles present within a cloud is important for determining accurate inputs to climate models. The chemical composition of particles within cloud liquid droplets and ice crystals can have a significant impact on the timing, location, and amount of precipitation that falls. Precipitation efficiency is increased by the presence of ice crystals in clouds, and both mineral dust and biological aerosols have been shown to be effective ice nucleating particles (INPs) in the atmosphere. A current challenge in aerosol science is distinguishing mineral dust and biological material in the analysis of real-time, ambient, single-particle mass spectral data. Single-particle mass spectrometers are capable of measuring the size-resolved chemical composition of individual atmospheric particles. However, there is no consistent analytical method for distinguishing dust and biological aerosols. Sampling and characterization of control samples (i.e. of known identity) of mineral dust and bacteria were performed by the Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) as part of the Fifth Ice Nucleation (FIN01) Workshop at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) facility in Karlsruhe, Germany. Using data collected by the ATOFMS of control samples, a new metric has been developed to classify single particles as dust or biological independent of spectral cluster analysis. This method, involving the use of a ratio of mass spectral peak areas for organic nitrogen and silicates, is easily reproducible and does not rely on extensive knowledge of particle chemistry or the ionization characteristics of mass spectrometers. This represents a step toward rapidly distinguishing particle types responsible for ice nucleation activity during real-time sampling in clouds. The ability to distinguish types of particles present within a cloud is important for determining accurate inputs to climate models. The chemical composition of particles within cloud liquid droplets and ice crystals can have a significant impact on the timing, location, and amount of precipitation that falls. Precipitation efficiency is increased by the presence of ice crystals in clouds, and both mineral dust and biological aerosols have been shown to be effective ice nucleating particles (INPs) in the atmosphere. A current challenge in aerosol science is distinguishing mineral dust and biological material in the analysis of real-time, ambient, single-particle mass spectral data. Single-particle mass spectrometers are capable of measuring the size-resolved chemical composition of individual atmospheric particles. However, there is no consistent analytical method for distinguishing dust and biological aerosols. Sampling and characterization of control samples (i.e. of known identity) of mineral dust and bacteria were performed by the Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) as part of the Fifth Ice Nucleation (FIN01) Workshop at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) facility in Karlsruhe, Germany. Using data collected by the ATOFMS of control samples, a new metric has been developed to classify single particles as dust or biological independent of spectral cluster analysis. This method, involving the use of a ratio of mass spectral peak areas for organic nitrogen and silicates, is easily reproducible and does not rely on extensive knowledge of particle chemistry or the ionization characteristics of mass spectrometers. This represents a step toward rapidly distinguishing particle types responsible for ice nucleation activity during real-time sampling in clouds.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Subgroup Analysis in Burnout: Relations Between Fatigue, Anxiety, and Depression

PubMed Central

van Dam, Arno

2016-01-01

Several authors have suggested that burned out patients do not form a homogeneous group and that subgroups should be considered. The identification of these subgroups may contribute to a better understanding of the burnout construct and lead to more specific therapeutic interventions. Subgroup analysis may also help clarify whether burnout is a distinct entity and whether subgroups of burnout overlap with other disorders such as depression and chronic fatigue syndrome. In a group of 113 clinically diagnosed burned out patients, levels of fatigue, depression, and anxiety were assessed. In order to identify possible subgroups, we performed a two-step cluster analysis. The analysis revealed two clusters that differed from one another in terms of symptom severity on the three aforementioned measures. Depression appeared to be the strongest predictor of group membership. These results are considered in the light of the scientific debate on whether burnout can be distinguished from depression and whether burnout subtyping is useful. Finally, implications for clinical practice and future research are discussed. PMID:26869983

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder.

PubMed

Bauer, M; Glenn, T; Alda, M; Andreassen, O A; Angelopoulos, E; Ardau, R; Baethge, C; Bauer, R; Bellivier, F; Belmaker, R H; Berk, M; Bjella, T D; Bossini, L; Bersudsky, Y; Cheung, E Y W; Conell, J; Del Zompo, M; Dodd, S; Etain, B; Fagiolini, A; Frye, M A; Fountoulakis, K N; Garneau-Fournier, J; Gonzalez-Pinto, A; Harima, H; Hassel, S; Henry, C; Iacovides, A; Isometsä, E T; Kapczinski, F; Kliwicki, S; König, B; Krogh, R; Kunz, M; Lafer, B; Larsen, E R; Lewitzka, U; Lopez-Jaramillo, C; MacQueen, G; Manchia, M; Marsh, W; Martinez-Cengotitabengoa, M; Melle, I; Monteith, S; Morken, G; Munoz, R; Nery, F G; O'Donovan, C; Osher, Y; Pfennig, A; Quiroz, D; Ramesar, R; Rasgon, N; Reif, A; Ritter, P; Rybakowski, J K; Sagduyu, K; Scippa, A M; Severus, E; Simhandl, C; Stein, D J; Strejilevich, S; Hatim Sulaiman, A; Suominen, K; Tagata, H; Tatebayashi, Y; Torrent, C; Vieta, E; Viswanath, B; Wanchoo, M J; Zetin, M; Whybrow, P C

2015-01-01

Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

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

Moran, James J.; Ehrhardt, Christopher J.; Wahl, Jon H.

We analyzed 21 neat acetone samples from 15 different suppliers to demonstrate the utility of a coupled stable isotope and trace contaminant strategy for distinguishing forensically-relevant samples. By combining these two pieces of orthogonal data we could discriminate all of the acetones that were produced by the 15 different suppliers. Using stable isotope ratios alone, we were able to distinguish 9 acetone samples, while the remaining 12 fell into four clusters with highly similar signatures. Adding trace chemical contaminant information enhanced discrimination to 13 individual acetones with three residual clusters. The acetones within each cluster shared a common manufacturer andmore » might, therefore, not be expected to be resolved. The data presented here demonstrates the power of combining orthogonal data sets to enhance sample fingerprinting and highlights the role disparate data could play in future forensic investigations.« less

The Regularity of Sustained Firing Reveals Two Populations of Slowly Adapting Touch Receptors in Mouse Hairy Skin

PubMed Central

Wellnitz, Scott A.; Lesniak, Daine R.; Gerling, Gregory J.

2010-01-01

Touch is initiated by diverse somatosensory afferents that innervate the skin. The ability to manipulate and classify receptor subtypes is prerequisite for elucidating sensory mechanisms. Merkel cell–neurite complexes, which distinguish shapes and textures, are experimentally tractable mammalian touch receptors that mediate slowly adapting type I (SAI) responses. The assessment of SAI function in mutant mice has been hindered because previous studies did not distinguish SAI responses from slowly adapting type II (SAII) responses, which are thought to arise from different end organs, such as Ruffini endings. Thus we sought methods to discriminate these afferent types. We developed an epidermis-up ex vivo skin–nerve chamber to record action potentials from afferents while imaging Merkel cells in intact receptive fields. Using model-based cluster analysis, we found that two types of slowly adapting receptors were readily distinguished based on the regularity of touch-evoked firing patterns. We identified these clusters as SAI (coefficient of variation = 0.78 ± 0.09) and SAII responses (0.21 ± 0.09). The identity of SAI afferents was confirmed by recording from transgenic mice with green fluorescent protein–expressing Merkel cells. SAI receptive fields always contained fluorescent Merkel cells (n = 10), whereas SAII receptive fields lacked these cells (n = 5). Consistent with reports from other vertebrates, mouse SAI and SAII responses arise from afferents exhibiting similar conduction velocities, receptive field sizes, mechanical thresholds, and firing rates. These results demonstrate that mice, like other vertebrates, have two classes of slowly adapting light-touch receptors, identify a simple method to distinguish these populations, and extend the utility of skin–nerve recordings for genetic dissection of touch receptor mechanisms. PMID:20393068

Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

NASA Astrophysics Data System (ADS)

Wu, Xin-Ye; Zheng, Zhi-Gang

2013-08-01

A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

Trace Element Study of H Chondrites: Evidence for Meteoroid Streams.

NASA Astrophysics Data System (ADS)

Wolf, Stephen Frederic

1993-01-01

Multivariate statistical analyses, both linear discriminant analysis and logistic regression, of the volatile trace elemental concentrations in H4-6 chondrites reveal compositionally distinguishable subpopulations. Observed difference in volatile trace element composition between Antarctic and non-Antarctic H4-6 chondrites (Lipschutz and Samuels, 1991) can be explained by a compositionaily distinct subpopulation found in Victoria Land, Antarctica. This population of H4-6 chondrites is compositionally distinct from non-Antarctic H4-6 chondrites and from Antarctic H4 -6 chondrites from Queen Maud Land. Comparisons of Queen Maud Land H4-6 chondrites with non-Antarctic H4-6 chondrites do not give reason to believe that these two populations are distinguishable from each other on the basis of the ten volatile trace element concentrations measured. ANOVA indicates that these differences are not the result of trivial causes such as weathering and analytical bias. Thermoluminescence properties of these populations parallels the results of volatile trace element comparisons. Given the differences in terrestrial age between Victoria Land, Queen Maud Land, and modern H4-6 chondrite falls, these results are consistent with a variation in H4-6 chondrite flux on a 300 ky timescale. This conclusion requires the existence of co-orbital meteoroid streams. Statistical analyses of the volatile trace elemental concentrations in non-Antarctic modern falls of H4-6 chondrites also demonstrate that a group of 13 H4-6 chondrites, Cluster 1, selected exclusively for their distinct fall parameters (Dodd, 1992) is compositionally distinguishable from a control group of 45 non-Antarctic modern H4-6 chondrites on the basis of the ten volatile trace element concentrations measured. Model-independent randomization-simulations based on both linear discriminant analysis and logistic regression verify these results. While ANOVA identifies two possible causes for this difference, analytical bias and group classification, a test validation experiment verifies that group classification is the more significant cause of compositional difference between Cluster 1 and non-Cluster 1 modern H4-6 chondrite falls. Thermoluminescence properties of these populations parallels the results of volatile trace element comparisons. This suggests that these meteorites are fragments of a co-orbital meteorite stream derived from a single parent body.

Chemical Fingerprint and Quantitative Analysis for the Quality Evaluation of Platycladi cacumen by Ultra-performance Liquid Chromatography Coupled with Hierarchical Cluster Analysis.

PubMed

Shan, Mingqiu; Li, Sam Fong Yau; Yu, Sheng; Qian, Yan; Guo, Shuchen; Zhang, Li; Ding, Anwei

2018-01-01

Platycladi cacumen (dried twigs and leaves of Platycladus orientalis (L.) Franco) is a frequently utilized Chinese medicinal herb. To evaluate the quality of the phytomedcine, an ultra-performance liquid chromatographic method with diode array detection was established for chemical fingerprinting and quantitative analysis. In this study, 27 batches of P. cacumen from different regions were collected for analysis. A chemical fingerprint with 20 common peaks was obtained using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A). Among these 20 components, seven flavonoids (myricitrin, isoquercitrin, quercitrin, afzelin, cupressuflavone, amentoflavone and hinokiflavone) were identified and determined simultaneously. In the method validation, the seven analytes showed good regressions (R ≥ 0.9995) within linear ranges and good recoveries from 96.4% to 103.3%. Furthermore, with the contents of these seven flavonoids, hierarchical clustering analysis was applied to distinguish the 27 batches into five groups. The chemometric results showed that these groups were almost consistent with geographical positions and climatic conditions of the production regions. Integrating fingerprint analysis, simultaneous determination and hierarchical clustering analysis, the established method is rapid, sensitive, accurate and readily applicable, and also provides a significant foundation for quality control of P. cacumen efficiently. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Co-citation Network Analysis of Religious Texts

NASA Astrophysics Data System (ADS)

Murai, Hajime; Tokosumi, Akifumi

This paper introduces a method of representing in a network the thoughts of individual authors of dogmatic texts numerically and objectively by means of co-citation analysis and a method of distinguishing between the thoughts of various authors by clustering and analysis of clustered elements, generated by the clustering process. Using these methods, this paper creates and analyzes the co-citation networks for five authoritative Christian theologians through history (Augustine, Thomas Aquinas, Jean Calvin, Karl Barth, John Paul II). These analyses were able to extract the core element of Christian thought (Jn 1:14, Ph 2:6, Ph 2:7, Ph 2:8, Ga 4:4), as well as distinctions between the individual theologians in terms of their sect (Catholic or Protestant) and era (thinking about the importance of God's creation and the necessity of spreading the Gospel). By supplementing conventional literary methods in areas such as philosophy and theology, with these numerical and objective methods, it should be possible to compare the characteristics of various doctrines. The ability to numerically and objectively represent the characteristics of various thoughts opens up the possibilities of utilizing new information technology, such as web ontology and the Artificial Intelligence, in order to process information about ideological thoughts in the future.

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage

PubMed Central

Lin, Ping-Chang; Reiter, David A.; Spencer, Richard G.

2010-01-01

MRI is increasingly used to evaluate cartilage in tissue constructs, explants, and animal and patient studies. However, while mean values of MR parameters, including T1, T2, magnetization transfer rate km, apparent diffusion coefficient ADC, and the dGEMRIC-derived fixed charge density, correlate with tissue status, the ability to classify tissue according to these parameters has not been explored. Therefore, the sensitivity and specificity with which each of these parameters was able to distinguish between normal and trypsin- degraded, and between normal and collagenase-degraded, cartilage explants were determined. Initial analysis was performed using a training set to determine simple group means to which parameters obtained from a validation set were compared. T1 and ADC showed the greatest ability to discriminate between normal and degraded cartilage. Further analysis with k-means clustering, which eliminates the need for a priori identification of sample status, generally performed comparably. Use of fuzzy c-means (FCM) clustering to define centroids likewise did not result in improvement in discrimination. Finally, a FCM clustering approach in which validation samples were assigned in a probabilistic fashion to control and degraded groups was implemented, reflecting the range of tissue characteristics seen with cartilage degradation. PMID:19705467

On the map: Nature and Science editorials.

PubMed

Waaijer, Cathelijn J F; van Bochove, Cornelis A; van Eck, Nees Jan

2011-01-01

Bibliometric mapping of scientific articles based on keywords and technical terms in abstracts is now frequently used to chart scientific fields. In contrast, no significant mapping has been applied to the full texts of non-specialist documents. Editorials in Nature and Science are such non-specialist documents, reflecting the views of the two most read scientific journals on science, technology and policy issues. We use the VOSviewer mapping software to chart the topics of these editorials. A term map and a document map are constructed and clusters are distinguished in both of them. The validity of the document clustering is verified by a manual analysis of a sample of the editorials. This analysis confirms the homogeneity of the clusters obtained by mapping and augments the latter with further detail. As a result, the analysis provides reliable information on the distribution of the editorials over topics, and on differences between the journals. The most striking difference is that Nature devotes more attention to internal science policy issues and Science more to the political influence of scientists. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11192-010-0205-9) contains supplementary material, which is available to authorized users.

Noise-free accurate count of microbial colonies by time-lapse shadow image analysis.

PubMed

Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hisakage; Saito, Mikako; Matsuoka, Hideaki

2012-12-01

Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance. Copyright © 2012 Elsevier B.V. All rights reserved.

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

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

Computational identification of developmental enhancers:conservation and function of transcription factor binding-site clustersin drosophila melanogaster and drosophila psedoobscura

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

Berman, Benjamin P.; Pfeiffer, Barret D.; Laverty, Todd R.

2004-08-06

Background The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. Results We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene,more » and assayed embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. Conclusions Measuring conservation of sequence features closely linked to function - such as binding-site clustering - makes better use of comparative sequence data than commonly used methods that examine only sequence identity.« less

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study.

PubMed

Nguyen, Huyen T; Shah, Zarine K; Mortazavi, Amir; Pohar, Kamal S; Wei, Lai; Jia, Guang; Zynger, Debra L; Knopp, Michael V

2017-05-01

To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder. Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient's lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test. Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening. The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer. • Heterogeneity is an intrinsic characteristic of tumoral tissue. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy. • Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity. • The quantification helps differentiate malignant from benign urinary bladder tissue.

Using radar imagery for crop discrimination: a statistical and conditional probability study

USGS Publications Warehouse

Haralick, R.M.; Caspall, F.; Simonett, D.S.

1970-01-01

A number of the constraints with which remote sensing must contend in crop studies are outlined. They include sensor, identification accuracy, and congruencing constraints; the nature of the answers demanded of the sensor system; and the complex temporal variances of crops in large areas. Attention is then focused on several methods which may be used in the statistical analysis of multidimensional remote sensing data.Crop discrimination for radar K-band imagery is investigated by three methods. The first one uses a Bayes decision rule, the second a nearest-neighbor spatial conditional probability approach, and the third the standard statistical techniques of cluster analysis and principal axes representation.Results indicate that crop type and percent of cover significantly affect the strength of the radar return signal. Sugar beets, corn, and very bare ground are easily distinguishable, sorghum, alfalfa, and young wheat are harder to distinguish. Distinguishability will be improved if the imagery is examined in time sequence so that changes between times of planning, maturation, and harvest provide additional discriminant tools. A comparison between radar and photography indicates that radar performed surprisingly well in crop discrimination in western Kansas and warrants further study.

Combining lead isotopes and cluster analysis to distinguish the Guarani and Serra Geral Aquifer Systems and contaminated waters in a highly industrialized area in Southern Brazil.

PubMed

Kuhn, Isadora Aumond; Roisenberg, Ari

2017-10-01

The Rio dos Sinos Watershed area is located at the Middle-West region of the Rio Grande do Sul State, Southern Brazil, along thirty two municipalities and affecting 1.5 million inhabitants and many important industrial centers. Three main aquifers are recognized in the study area: the unconfined-fractured Serra Geral Aquifer System, the porous Guarani Aquifer System, and the Permian Aquitard. This study aims to understand groundwater, surface water and human activity interactions in the Rio dos Sinos Watershed, evaluating the application of stable lead isotopic ratios analyzed for this propose. Thirty six groundwater samples, 8 surface water samples and 5 liquid effluents of tanneries and landfills samples were measured using a Thermal Ionization Mass Spectrometer Thermo-Finnigan and a Neptune Multi-Collector Inductively Coupled Plasma Mass Spectrometer. Groundwater isotopic ratios have a wider range compared to the surface water, with less radiogenic averages 208 Pb/ 204 Pb = 38.1837 vs 38.4050 (standard deviation = 0.2921 vs 0.1343) and 206 Pb/ 204 Pb = 18.2947 vs 18.4766 (standard deviation = 0.2215 vs 0.1059), respectively. Industrial liquid effluents (tanneries and industrial landfill) have averages 208 Pb/ 204 Pb = 38.1956 and 206 Pb/ 204 Pb = 18.3169, distinct from effluent samples of domestic sanitary landfill (averages 208 Pb/ 204 Pb = 38.2353 and 206 Pb/ 204 Pb = 18.6607). Hierarchical cluster analysis led to distinguish six groups of groundwater, representing the three aquifers that occur in the area, two clusters suggesting groundwater mixtures and one demonstrating a highly contaminated groundwater. By analyzing the cluster results and wells' stratigraphic profiles it was possible to distinguish the different aquifers in the area. The Serra Geral Aquifer System has 206 Pb/ 204 Pb ratios between 18.4718 and 18.7089; 207 Pb/ 204 Pb between 15.6692 and 15.6777; 208 Pb/ 204 Pb between 38.6826 and 38.7616; 207 Pb/ 206 Pb between 0.8372 and 0.8623; 208 Pb/ 206 Pb between 2.0671 and 2.0964 and the Guarani Aquifer System has a wider range ( 208 Pb/ 204 Pb ranged from 37.9393 to 38.1279 and 206 Pb/ 204 Pb ranged from 18.0892 to 18.3217). Water mixing of these two aquifer systems is reflected by transitional results. The results confirm that the hierarchical cluster analysis of lead isotopes is a useful tool to discriminate different aquifer conditions, reflecting mostly the influence of the natural lead isotopic composition of the aquifers instead of the anthropogenic activities (urban and industrial), except when the groundwater is highly contaminated by human activity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multivariate carbon and nitrogen stable isotope model for the reconstruction of prehistoric human diet.

PubMed

Froehle, A W; Kellner, C M; Schoeninger, M J

2012-03-01

Using a sample of published archaeological data, we expand on an earlier bivariate carbon model for diet reconstruction by adding bone collagen nitrogen stable isotope values (δ(15) N), which provide information on trophic level and consumption of terrestrial vs. marine protein. The bivariate carbon model (δ(13) C(apatite) vs. δ(13) C(collagen) ) provides detailed information on the isotopic signatures of whole diet and dietary protein, but is limited in its ability to distinguish between C(4) and marine protein. Here, using cluster analysis and discriminant function analysis, we generate a multivariate diet reconstruction model that incorporates δ(13) C(apatite) , δ(13) C(collagen) , and δ(15) N holistically. Inclusion of the δ(15) N data proves useful in resolving protein-related limitations of the bivariate carbon model, and splits the sample into five distinct dietary clusters. Two significant discriminant functions account for 98.8% of the sample variance, providing a multivariate model for diet reconstruction. Both carbon variables dominate the first function, while δ(15) N most strongly influences the second. Independent support for the functions' ability to accurately classify individuals according to diet comes from a small sample of experimental rats, which cluster as expected from their diets. The new model also provides a statistical basis for distinguishing between food sources with similar isotopic signatures, as in a previously analyzed archaeological population from Saipan (see Ambrose et al.: AJPA 104(1997) 343-361). Our model suggests that the Saipan islanders' (13) C-enriched signal derives mainly from sugarcane, not seaweed. Further development and application of this model can similarly improve dietary reconstructions in archaeological, paleontological, and primatological contexts. Copyright © 2011 Wiley Periodicals, Inc.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

PubMed

Alagoz, Celal; Cohen, Andrew R; Frisch, Daniel R; Tunç, Birkan; Phatharodom, Saran; Guez, Allon

2018-07-01

Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns. Copyright © 2018 Elsevier B.V. All rights reserved.

X-ray spectral observations of clusters of galaxies undergoing merger events

NASA Astrophysics Data System (ADS)

Henriksen, Mark J.

1993-09-01

We have analyzed the HEAO 1 A2 observations of two clusters whose optical and X-ray isophotes are suggestive of merging subclusters, A119 and A754, and find evidence of nonisothermal X-ray emission from both clusters. The X-ray spectrum of both clusters, when fitted with a single isothermal model, shows residual soft X-ray emission. There is a statistically significant reduction in chi-squared (98 percent probability based on the F-test) when a second temperature component is added. If the asymmetric isophotes seen in the soft X-ray image are indicative of merging subclusters, then our analysis of the Einstein IPC spectra and Solid State Spectrometer observations of A754, which provide some spatial and spectral resolution, suggests that the two temperature components seen in the HEAO 1 A2 spectra are associated with gas trapped in the subcluster potential wells. The implied subcluster isothermal masses suggest that a more massive cluster is accreting a less massive companion in A754. The present observations cannot rule out the alternative possibility that the cooler gas is associated with the outer cluster atmosphere rather than individual subclusters, as appears to be the case for A119. Astro D observations will be necessary to distinguish between these two possibilities for both clusters.

Investigating the relationship between raw milk bacterial composition, as described by intergenic transcribed spacer-PCR fingerprinting, and pasture altitude.

PubMed

Bonizzi, I; Buffoni, J N; Feligini, M; Enne, G

2009-10-01

To assess the bacterial biodiversity level in bovine raw milk used to produce Fontina, a Protected Designation of Origin cheese manufactured at high-altitude pastures and in valleys of Valle d'Aosta region (North-western Italian Alps) without any starters. To study the relation between microbial composition and pasture altitude, in order to distinguish high-altitude milk against valley and lowland milk. The microflora from milks sampled at different alpine pasture, valley and lowland farms were fingerprinted by PCR of the 16S-23S intergenic transcribed spacers (ITS-PCR). The resulting band patterns were analysed by generalized multivariate statistical techniques to handle discrete (band presence-absence) and continuous (altitude) information. The fingerprints featured numerous bands and marked variability indicating complex, differentiated bacterial communities. Alpine pasture milks were distinguished from lowland ones by cluster analysis, while this technique less clearly discriminated alpine pasture and valley samples. Generalized principal component analysis and clustering-after-ordination enabled a more effective distinction of alpine pasture, valley and lowland samples. Alpine raw milks for Fontina production contain highly diverse bacterial communities, the composition of which is related to the altitude of the pasture where milk was produced. This research may provide analytical support to the important issue represented by the authentication of the geographical origin of alpine milk productions.

Sequence-related amplified polymorphism (SRAP) marker as a new method for identification of endophytic fungi from Taxus.

PubMed

Ren, Na; Liu, Jiajia; Yang, Dongliang; Chen, Jianhua; Luan, Mingbao; Hong, Juan

2012-01-01

A total of 20 endophytic fungi stains were classified into four groups using traditional morphological identification method, and were studied for genetic diversity by sequence-related amplified polymorphism (SRAP) technique. Genomic DNA (deoxyribonucleic acid) of these strains was extracted with CTAB method. SRAP analysis was done with 24 pairs of primers. All strains could be uniquely distinguished with 584 bands and 446 polymorphism bands which generated 76.4% of polymorphic ratio. Unweighted pair-group method with arithmetical averages cluster analysis enabled construction of a dendrogram for estimating genetic distances between different strains. All strains, which were just divided into four groups by traditional morphology identification, were clustered into four major groups at GS = 0.603 and further separated into eight sub-groups at GS = 0.921. Dendrogram also revealed a large genetic variation in 20 strains; different primer combinations allowed them distinctly distinguished one from others with relatively low genetic similarity. The results show that the SRAP technology is more efficient than traditional morphology identification. It is found that SRAP markers could more really reflect the genetic diversity of endophytic fungi strains from Taxus, and also could be used as a method for identification of endophytic fungi from Taxus. It also suggests that SRAP can be used to establish foundation for further screening of taxol-producing endophytic fungi strains which can produce high levels of paclitaxel.

Sequence determination and analysis of S-adenosyl-L-homocysteine hydrolase from yellow lupine (Lupinus luteus).

PubMed

Brzeziński, K; Janowski, R; Podkowiński, J; Jaskólski, M

2001-01-01

The coding sequences of two S-adenosyl-L-homocysteine hydrolases (SAHases) were identified in yellow lupine by screenig of a cDNA library. One of them, corresponding to the complete protein, was sequenced and compared with 52 other SAHase sequences. Phylogenetic analysis of these proteins identified three groups of the enzymes. Group A comprises only bacterial sequences. Group B is subdivided into two subgroups, one of which (B1) is formed by animal sequences. Subgroup B2 consist of two distinct clusters, B2a and B2b. Cluster B2b comprises all known plant sequences, including the yellow lupine enzyme, which are distinguished by a 50-residue insert. Group C is heterogeneous and contains SAHases from Archaea as well as a new class of animal enzymes, distinctly different from those in group B1.

Tongue Color Analysis for Medical Application

PubMed Central

Wang, Xingzheng; You, Jane

2013-01-01

An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color. PMID:23737824

Pollen assemblages as paleoenvironmental proxies in the Florida Everglades

USGS Publications Warehouse

Willard, D.A.; Weimer, L.M.; Riegel, W.L.

2001-01-01

Analysis of 170 pollen assemblages from surface samples in eight vegetation types in the Florida Everglades indicates that these wetland sub-environments are distinguishable from the pollen record and that they are useful proxies for hydrologic and edaphic parameters. Vegetation types sampled include sawgrass marshes, cattail marshes, sloughs with floating aquatics, wet prairies, brackish marshes, tree islands, cypress swamps, and mangrove forests. The distribution of these vegetation types is controlled by specific environmental parameters, such as hydrologic regime, nutrient availability, disturbance level, substrate type, and salinity; ecotones between vegetation types may be sharp. Using R-mode cluster analysis of pollen data, we identified diagnostic species groupings; Q-mode cluster analysis was used to differentiate pollen signatures of each vegetation type. Cluster analysis and the modern analog technique were applied to interpret vegetational and environmental trends over the last two millennia at a site in Water Conservation Area 3A. The results show that close modern analogs exist for assemblages in the core and indicate past hydrologic changes at the site, correlated with both climatic and land-use changes. The ability to differentiate marshes with different hydrologic and edaphic requirements using the pollen record facilitates assessment of relative impacts of climatic and anthropogenic changes on this wetland ecosystem on smaller spatial and temporal scales than previously were possible. ?? 2001 Elsevier Science B.V.

Social phobia subtypes in the general population revealed by cluster analysis.

PubMed

Furmark, T; Tillfors, M; Stattin, H; Ekselius, L; Fredrikson, M

2000-11-01

Epidemiological data on subtypes of social phobia are scarce and their defining features are debated. Hence, the present study explored the prevalence and descriptive characteristics of empirically derived social phobia subgroups in the general population. To reveal subtypes, data on social distress, functional impairment, number of social fears and criteria fulfilled for avoidant personality disorder were extracted from a previously published epidemiological study of 188 social phobics and entered into an hierarchical cluster analysis. Criterion validity was evaluated by comparing clusters on the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Finally, profile analyses were performed in which clusters were compared on a set of sociodemographic and descriptive characteristics. Three clusters emerged, consisting of phobics scoring either high (generalized subtype), intermediate (non-generalized subtype) or low (discrete subtype) on all variables. Point prevalence rates were 2.0%, 5.9% and 7.7% respectively. All subtypes were distinguished on both SPS and SIAS. Generalized or severe social phobia tended to be over-represented among individuals with low levels of educational attainment and social support. Overall, public-speaking was the most common fear. Although categorical distinctions may be used, the present data suggest that social phobia subtypes in the general population mainly differ dimensionally along a mild moderate-severe continuum, and that the number of cases declines with increasing severity.

Structural parameters of young star clusters: fractal analysis

NASA Astrophysics Data System (ADS)

Hetem, A.

2017-07-01

A unified view of star formation in the Universe demand detailed and in-depth studies of young star clusters. This work is related to our previous study of fractal statistics estimated for a sample of young stellar clusters (Gregorio-Hetem et al. 2015, MNRAS 448, 2504). The structural properties can lead to significant conclusions about the early stages of cluster formation: 1) virial conditions can be used to distinguish warm collapsed; 2) bound or unbound behaviour can lead to conclusions about expansion; and 3) fractal statistics are correlated to the dynamical evolution and age. The technique of error bars estimation most used in the literature is to adopt inferential methods (like bootstrap) to estimate deviation and variance, which are valid only for an artificially generated cluster. In this paper, we expanded the number of studied clusters, in order to enhance the investigation of the cluster properties and dynamic evolution. The structural parameters were compared with fractal statistics and reveal that the clusters radial density profile show a tendency of the mean separation of the stars increase with the average surface density. The sample can be divided into two groups showing different dynamic behaviour, but they have the same dynamic evolution, since the entire sample was revealed as being expanding objects, for which the substructures do not seem to have been completely erased. These results are in agreement with the simulations adopting low surface densities and supervirial conditions.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Surprising performance for vibrational frequencies of the distinguishable clusters with singles and doubles (DCSD) and MP2.5 approximations

NASA Astrophysics Data System (ADS)

Kesharwani, Manoj K.; Sylvetsky, Nitai; Martin, Jan M. L.

2017-11-01

We show that the DCSD (distinguishable clusters with all singles and doubles) correlation method permits the calculation of vibrational spectra at near-CCSD(T) quality but at no more than CCSD cost, and with comparatively inexpensive analytical gradients. For systems dominated by a single reference configuration, even MP2.5 is a viable alternative, at MP3 cost. MP2.5 performance for vibrational frequencies is comparable to double hybrids such as DSD-PBEP86-D3BJ, but without resorting to empirical parameters. DCSD is also quite suitable for computing zero-point vibrational energies in computational thermochemistry.

Chemotaxonomy of heterocystous cyanobacteria using FAME profiling as species markers.

PubMed

Shukla, Ekta; Singh, Satya Shila; Singh, Prashant; Mishra, Arun Kumar

2012-07-01

The fatty acid methyl ester (FAME) analysis of the 12 heterocystous cyanobacterial strains showed different fatty acid profiling based on the presence/absence and the percentage of 13 different types of fatty acids. The major fatty acids viz. palmitic acid (16:0), hexadecadienoic acid (16:2), stearic acid (18:0), oleic acid (18:1), linoleic (18:2), and linolenic acid (18:3) were present among all the strains except Cylindrospermum musicola where oleic acid (18:1) was absent. All the strains showed high levels of polyunsaturated fatty acid (PUFAs; 41-68.35%) followed by saturated fatty acid (SAFAs; 1.82-40.66%) and monounsaturated fatty acid (0.85-24.98%). Highest percentage of PUFAs and essential fatty acid (linolenic acid; 18:3) was reported in Scytonema bohnerii which can be used as fatty acid supplement in medical and biotechnological purpose. The cluster analysis based on FAME profiling suggests the presence of two distinct clusters with Euclidean distance ranging from 0 to 25. S. bohnerii of cluster I was distantly related to the other strains of cluster II. The genotypes of cluster II were further divided into two subclusters, i.e., IIa with C. musicola showing great divergence with the other genotypes of IIb which was further subdivided into two groups. Subsubcluster IIb(1) was represented by a genotype, Anabaena sp. whereas subsubcluster IIb(2) was distinguished by two groups, i.e., one group having significant similarity among their three genotypes showed distant relation with the other group having closely related six genotypes. To test the validity of the fatty acid profiles as a marker, cluster analysis has also been generated on the basis of morphological attributes. Our results suggest that FAME profiling might be used as species markers in the study of polyphasic approach based taxonomy and phylogenetic relationship.

Hunting for origins of migraine pain: cluster analysis of spontaneous and capsaicin-induced firing in meningeal trigeminal nerve fibers

PubMed Central

Zakharov, A.; Vitale, C.; Kilinc, E.; Koroleva, K.; Fayuk, D.; Shelukhina, I.; Naumenko, N.; Skorinkin, A.; Khazipov, R.; Giniatullin, R.

2015-01-01

Trigeminal nerves in meninges are implicated in generation of nociceptive firing underlying migraine pain. However, the neurochemical mechanisms of nociceptive firing in meningeal trigeminal nerves are little understood. In this study, using suction electrode recordings from peripheral branches of the trigeminal nerve in isolated rat meninges, we analyzed spontaneous and capsaicin-induced orthodromic spiking activity. In control, biphasic single spikes with variable amplitude and shapes were observed. Application of the transient receptor potential vanilloid 1 (TRPV1) agonist capsaicin to meninges dramatically increased firing whereas the amplitudes and shapes of spikes remained essentially unchanged. This effect was antagonized by the specific TRPV1 antagonist capsazepine. Using the clustering approach, several groups of uniform spikes (clusters) were identified. The clustering approach combined with capsaicin application allowed us to detect and to distinguish “responder” (65%) from “non-responder” clusters (35%). Notably, responders fired spikes at frequencies exceeding 10 Hz, high enough to provide postsynaptic temporal summation of excitation at brainstem and spinal cord level. Almost all spikes were suppressed by tetrodotoxin (TTX) suggesting an involvement of the TTX-sensitive sodium channels in nociceptive signaling at the peripheral branches of trigeminal neurons. Our analysis also identified transient (desensitizing) and long-lasting (slowly desensitizing) responses to the continuous application of capsaicin. Thus, the persistent activation of nociceptors in capsaicin-sensitive nerve fibers shown here may be involved in trigeminal pain signaling and plasticity along with the release of migraine-related neuropeptides from TRPV1 positive neurons. Furthermore, cluster analysis could be widely used to characterize the temporal and neurochemical profiles of other pain transducers likely implicated in migraine. PMID:26283923

A cluster-analytic study of substance problems and mental health among street youths.

PubMed

Adlaf, E M; Zdanowicz, Y M

1999-11-01

Based on a cluster analysis of 211 street youths aged 13-24 years interviewed in 1992 in Toronto, Ontario, Canada, we describe the configuration of mental health and substance use outcomes. Eight clusters were suggested: Entrepreneurs (n = 19) were frequently involved in delinquent activity and were highly entrenched in the street lifestyle; Drifters (n = 35) had infrequent social contact, displayed lower than average family dysfunction, and were not highly entrenched in the street lifestyle; Partiers (n = 40) were distinguished by their recreational motivation for alcohol and drug use and their below average entrenchment in the street lifestyle; Retreatists (n = 32) were distinguished by their high coping motivation for substance use; Fringers (n = 48) were involved marginally in the street lifestyle and showed lower than average family dysfunction; Transcenders (n = 21), despite above average physical and sexual abuse, reported below average mental health or substance use problems; Vulnerables (n = 12) were characterized by high family dysfunction (including physical and sexual abuse), elevated mental health outcomes, and use of alcohol and other drugs motivated by coping and escapism; Sex Workers (n = 4) were highly entrenched in the street lifestyle and reported frequent commercial sexual work, above average sexual abuse, and extensive use of crack cocaine. The results showed that distress, self-esteem, psychotic thoughts, attempted suicide, alcohol problems, drug problems, dual substance problems, and dual disorders varied significantly among the eight clusters. Overall, the findings suggest the need for differential programming. The data showed that risk factors, mental health, and substance use outcomes vary among this population. Also, for some the web of mental health and substance use problems is inseparable.

Near-infrared spectroscopy of candidate red supergiant stars in clusters

NASA Astrophysics Data System (ADS)

Messineo, Maria; Zhu, Qingfeng; Ivanov, Valentin D.; Figer, Donald F.; Davies, Ben; Menten, Karl M.; Kudritzki, Rolf P.; Chen, C.-H. Rosie

2014-11-01

Context. Clear identifications of Galactic young stellar clusters farther than a few kpc from the Sun are rare, despite the large number of candidate clusters. Aims: We aim to improve the selection of candidate clusters rich in massive stars with a multiwavelength analysis of photometric Galactic data that range from optical to mid-infrared wavelengths. Methods: We present a photometric and spectroscopic analysis of five candidate stellar clusters, which were selected as overdensities with bright stars (Ks< 7 mag) in GLIMPSE and 2MASS images. Results: A total of 48 infrared spectra were obtained. The combination of photometry and spectroscopy yielded six new red supergiant stars with masses from 10 M⊙ to 15 M⊙. Two red supergiants are located at Galactic coordinates (l,b) = (16.°7, -0.°63) and at a distance of about ~3.9 kpc; four other red supergiants are members of a cluster at Galactic coordinates (l,b) = (49.°3, + 0.°72) and at a distance of ~7.0 kpc. Conclusions: Spectroscopic analysis of the brightest stars of detected overdensities and studies of interstellar extinction along their line of sights are fundamental to distinguish regions of low extinction from actual stellar clusters. The census of young star clusters containing red supergiants is incomplete; in the existing all-sky near-infrared surveys, they can be identified as overdensities of bright stars with infrared color-magnitude diagrams characterized by gaps. Based on observations collected at the European Southern Observatory (ESO Programme 60.A-9700(E), and 089.D-0876), and on observations collected at the UKIRT telescope (programme ID H243NS).MM is currently employed by the MPIfR. Part of this work was performed at RIT (2009), at ESA (2010), and at the MPIfR.Tables 3, 4, and 6 are available in electronic form at http://www.aanda.org

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

PubMed

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

2006-07-01

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

Cluster analysis of the organic peaks in bulk mass spectra obtained during the 2002 New England Air Quality Study with an Aerodyne aerosol mass spectrometer

NASA Astrophysics Data System (ADS)

Marcolli, C.; Canagaratna, M. R.; Worsnop, D. R.; Bahreini, R.; de Gouw, J. A.; Warneke, C.; Goldan, P. D.; Kuster, W. C.; Williams, E. J.; Lerner, B. M.; Roberts, J. M.; Meagher, J. F.; Fehsenfeld, F. C.; Marchewka, M. L.; Bertman, S. B.; Middlebrook, A. M.

2006-06-01

We applied hierarchical cluster analysis to an Aerodyne aerosol mass spectrometer (AMS) bulk mass spectral dataset collected aboard the NOAA research vessel Ronald H. Brown during the 2002 New England Air Quality Study off the east coast of the United States. Emphasizing the organic peaks, the cluster analysis yielded a series of categories that are distinguishable with respect to their mass spectra and their occurrence as a function of time. The differences between the categories mainly arise from relative intensity changes rather than from the presence or absence of specific peaks. The most frequent category exhibits a strong signal at m/z 44 and represents oxidized organic matter most probably originating from both, anthropogenic as well as biogenic sources. On the basis of spectral and trace gas correlations, the second most common category with strong signals at m/z 29, 43, and 44 contains contributions from isoprene oxidation products. The third through the fifth most common categories have peak patterns characteristic of monoterpene oxidation products and were most frequently observed when air masses from monoterpene rich regions were sampled. Taken together, the second through the fifth most common categories represent as much as 5 µg/m3 organic aerosol mass - 17% of the total organic mass - that can be attributed to biogenic sources. These numbers have to be viewed as lower limits since the most common category was attributed to anthropogenic sources for this calculation. The cluster analysis was also very effective in identifying a few contaminated mass spectra that were not removed during pre-processing. This study demonstrates that hierarchical clustering is a useful tool to analyze the complex patterns of the organic peaks in bulk aerosol mass spectra from a field study.

A new application of hierarchical cluster analysis to investigate organic peaks in bulk mass spectra obtained with an Aerodyne Aerosol Mass Spectrometer

NASA Astrophysics Data System (ADS)

Middlebrook, A. M.; Marcolli, C.; Canagaratna, M. R.; Worsnop, D. R.; Bahreini, R.; de Gouw, J. A.; Warneke, C.; Goldan, P. D.; Kuster, W. C.; Williams, E. J.; Lerner, B. M.; Roberts, J. M.; Meagher, J. F.; Fehsenfeld, F. C.; Marchewka, M. L.; Bertman, S. B.

2006-12-01

We applied hierarchical cluster analysis to an Aerodyne aerosol mass spectrometer (AMS) bulk mass spectral dataset collected aboard the NOAA research vessel Ronald H. Brown during the 2002 New England Air Quality Study off the east coast of the United States. Emphasizing the organic peaks, the cluster analysis yielded a series of categories that are distinguishable with respect to their mass spectra and their occurrence as a function of time. The differences between the categories mainly arise from relative intensity changes rather than from the presence or absence of specific peaks. The most frequent category exhibits a strong signal at m/z 44 and represents oxidized organic matter probably originating from both anthropogenic as well as biogenic sources. On the basis of spectral and trace gas correlations, the second most common category with strong signals at m/z 29, 43, and 44 contains contributions from isoprene oxidation products. The third through the fifth most common categories have peak patterns characteristic of monoterpene oxidation products and were most frequently observed when air masses from monoterpene rich regions were sampled. Taken together, the second through the fifth most common categories represent on average 17% of the total organic mass that stems likely from biogenic sources during the ship's cruise. These numbers have to be viewed as lower limits since the most common category was attributed to anthropogenic sources for this calculation. The cluster analysis was also very effective in identifying a few contaminated mass spectra that were not removed during pre-processing. This study demonstrates that hierarchical clustering is a useful tool to analyze the complex patterns of the organic peaks in bulk aerosol mass spectra from a field study.

Cluster Analysis of the Organic Peaks in Bulk Mass Spectra Obtained During the 2002 New England Air Quality Study with an Aerodyne Aerosol Mass Spectrometer

NASA Astrophysics Data System (ADS)

Marcolli, C.; Canagaratna, M. R.; Worsnop, D. R.; Bahreini, R.; de Gouw, J. A.; Warneke, C.; Goldan, P. D.; Kuster, W. C.; Williams, E. J.; Lerner, B. M.; Roberts, J. M.; Meagher, J. F.; Fehsenfeld, F. C.; Marchewka, M.; Bertman, S. B.; Middlebrook, A. M.

2006-12-01

We applied hierarchical cluster analysis to an Aerodyne aerosol mass spectrometer (AMS) bulk mass spectral dataset collected aboard the NOAA research vessel R. H. Brown during the 2002 New England Air Quality Study off the east coast of the United States. Emphasizing the organic peaks, the cluster analysis yielded a series of categories that are distinguishable with respect to their mass spectra and their occurrence as a function of time. The differences between the categories mainly arise from relative intensity changes rather than from the presence or absence of specific peaks. The most frequent category exhibits a strong signal at m/z 44 and represents oxidized organic matter probably originating from both anthropogenic as well as biogenic sources. On the basis of spectral and trace gas correlations, the second most common category with strong signals at m/z 29, 43, and 44 contains contributions from isoprene oxidation products. The third through the fifth most common categories have peak patterns characteristic of monoterpene oxidation products and were most frequently observed when air masses from monoterpene rich regions were sampled. Taken together, the second through the fifth most common categories represent on average 17% of the total organic mass that stems likely from biogenic sources during the ship's cruise. These numbers have to be viewed as lower limits since the most common category was attributed to anthropogenic sources for this calculation. The cluster analysis was also very effective in identifying a few contaminated mass spectra that were not removed during pre-processing. This study demonstrates that hierarchical clustering is a useful tool to analyze the complex patterns of the organic peaks in bulk aerosol mass spectra from a field study.

Limitations of cytochrome oxidase I for the barcoding of Neritidae (Mollusca: Gastropoda) as revealed by Bayesian analysis.

PubMed

Chee, S Y

2015-05-25

The mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) gene has been universally and successfully utilized as a barcoding gene, mainly because it can be amplified easily, applied across a wide range of taxa, and results can be obtained cheaply and quickly. However, in rare cases, the gene can fail to distinguish between species, particularly when exposed to highly sensitive methods of data analysis, such as the Bayesian method, or when taxa have undergone introgressive hybridization, over-splitting, or incomplete lineage sorting. Such cases require the use of alternative markers, and nuclear DNA markers are commonly used. In this study, a dendrogram produced by Bayesian analysis of an mtDNA COI dataset was compared with that of a nuclear DNA ATPS-α dataset, in order to evaluate the efficiency of COI in barcoding Malaysian nerites (Neritidae). In the COI dendrogram, most of the species were in individual clusters, except for two species: Nerita chamaeleon and N. histrio. These two species were placed in the same subcluster, whereas in the ATPS-α dendrogram they were in their own subclusters. Analysis of the ATPS-α gene also placed the two genera of nerites (Nerita and Neritina) in separate clusters, whereas COI gene analysis placed both genera in the same cluster. Therefore, in the case of the Neritidae, the ATPS-α gene is a better barcoding gene than the COI gene.

Contrast improvement of terahertz images of thin histopathologic sections

PubMed Central

Formanek, Florian; Brun, Marc-Aurèle; Yasuda, Akio

2011-01-01

We present terahertz images of 10 μm thick histopathologic sections obtained in reflection geometry with a time-domain spectrometer, and demonstrate improved contrast for sections measured in paraffin with water. Automated segmentation is applied to the complex refractive index data to generate clustered terahertz images distinguishing cancer from healthy tissues. The degree of classification of pixels is then evaluated using registered visible microscope images. Principal component analysis and propagation simulations are employed to investigate the origin and the gain of image contrast. PMID:21326635

Contrast improvement of terahertz images of thin histopathologic sections.

PubMed

Formanek, Florian; Brun, Marc-Aurèle; Yasuda, Akio

2010-12-03

We present terahertz images of 10 μm thick histopathologic sections obtained in reflection geometry with a time-domain spectrometer, and demonstrate improved contrast for sections measured in paraffin with water. Automated segmentation is applied to the complex refractive index data to generate clustered terahertz images distinguishing cancer from healthy tissues. The degree of classification of pixels is then evaluated using registered visible microscope images. Principal component analysis and propagation simulations are employed to investigate the origin and the gain of image contrast.

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

Witt, R. G.; Bly, B. G.; Campbell, W. J.; Bloemer, H. H. L.; Brumfield, J. O.

1982-01-01

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

Measuring Spatial Dependence for Infectious Disease Epidemiology

PubMed Central

Grabowski, M. Kate; Cummings, Derek A. T.

2016-01-01

Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases. PMID:27196422

Robust fiber clustering of cerebral fiber bundles in white matter

NASA Astrophysics Data System (ADS)

Yao, Xufeng; Wang, Yongxiong; Zhuang, Songlin

2014-11-01

Diffusion tensor imaging fiber tracking (DTI-FT) has been widely accepted in the diagnosis and treatment of brain diseases. During the rendering pipeline of specific fiber tracts, the image noise and low resolution of DTI would lead to false propagations. In this paper, we propose a robust fiber clustering (FC) approach to diminish false fibers from one fiber tract. Our algorithm consists of three steps. Firstly, the optimized fiber assignment continuous tracking (FACT) is implemented to reconstruct one fiber tract; and then each curved fiber in the fiber tract is mapped to a point by kernel principal component analysis (KPCA); finally, the point clouds of fiber tract are clustered by hierarchical clustering which could distinguish false fibers from true fibers in one tract. In our experiment, the corticospinal tract (CST) in one case of human data in vivo was used to validate our method. Our method showed reliable capability in decreasing the false fibers in one tract. In conclusion, our method could effectively optimize the visualization of fiber bundles and would help a lot in the field of fiber evaluation.

Simultaneous determination of 19 flavonoids in commercial trollflowers by using high-performance liquid chromatography and classification of samples by hierarchical clustering analysis.

PubMed

Song, Zhiling; Hashi, Yuki; Sun, Hongyang; Liang, Yi; Lan, Yuexiang; Wang, Hong; Chen, Shizhong

2013-12-01

The flowers of Trollius species, named Jin Lianhua in Chinese, are widely used traditional Chinese herbs with vital biological activity that has been used for several decades in China to treat upper respiratory infections, pharyngitis, tonsillitis, and bronchitis. We developed a rapid and reliable method for simultaneous quantitative analysis of 19 flavonoids in trollflowers by using high-performance liquid chromatography (HPLC). Chromatography was performed on Inertsil ODS-3 C18 column, with gradient elution methanol-acetonitrile-water with 0.02% (v/v) formic acid. Content determination was used to evaluate the quality of commercial trollflowers from different regions in China, while three Trollius species (Trollius chinensis Bunge, Trollius ledebouri Reichb, Trollius buddae Schipcz) were explicitly distinguished by using hierarchical clustering analysis. The linearity, precision, accuracy, limit of detection, and limit of quantification were validated for the quantification method, which proved sensitive, accurate and reproducible indicating that the proposed approach was applicable for the routine analysis and quality control of trollflowers. © 2013.

Integration of stable isotope and trace contaminant concentration for enhanced forensic acetone discrimination.

PubMed

Moran, James J; Ehrhardt, Christopher J; Wahl, Jon H; Kreuzer, Helen W; Wahl, Karen L

2013-11-15

We analyzed 21 neat acetone samples from 15 different suppliers to demonstrate the utility of a coupled stable isotope and trace contaminant strategy for distinguishing forensically-relevant samples. By combining these two pieces of orthogonal data we could discriminate all of the acetones that were produced by the 15 different suppliers. Using stable isotope ratios alone, we were able to distinguish 8 acetone samples, while the remaining 13 fell into four clusters with highly similar signatures. Adding trace chemical contaminant information enhanced discrimination to 13 individual acetones with three residual clusters. The acetones within each cluster shared a common manufacturer and might, therefore, not be expected to be resolved. The data presented here demonstrates the power of combining orthogonal data sets to enhance sample fingerprinting and highlights the role disparate data could play in future forensic investigations. © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Hyperspectral imaging coupled with chemometric analysis for non-invasive differentiation of black pens

NASA Astrophysics Data System (ADS)

Chlebda, Damian K.; Majda, Alicja; Łojewski, Tomasz; Łojewska, Joanna

2016-11-01

Differentiation of the written text can be performed with a non-invasive and non-contact tool that connects conventional imaging methods with spectroscopy. Hyperspectral imaging (HSI) is a relatively new and rapid analytical technique that can be applied in forensic science disciplines. It allows an image of the sample to be acquired, with full spectral information within every pixel. For this paper, HSI and three statistical methods (hierarchical cluster analysis, principal component analysis, and spectral angle mapper) were used to distinguish between traces of modern black gel pen inks. Non-invasiveness and high efficiency are among the unquestionable advantages of ink differentiation using HSI. It is also less time-consuming than traditional methods such as chromatography. In this study, a set of 45 modern gel pen ink marks deposited on a paper sheet were registered. The spectral characteristics embodied in every pixel were extracted from an image and analysed using statistical methods, externally and directly on the hypercube. As a result, different black gel inks deposited on paper can be distinguished and classified into several groups, in a non-invasive manner.

Factors that cause genotype by environment interaction and use of a multiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia.

PubMed

Cerón-Muñoz, M F; Tonhati, H; Costa, C N; Rojas-Sarmiento, D; Echeverri Echeverri, D M

2004-08-01

Descriptive herd variables (DVHE) were used to explain genotype by environment interactions (G x E) for milk yield (MY) in Brazilian and Colombian production environments and to develop a herd-cluster model to estimate covariance components and genetic parameters for each herd environment group. Data consisted of 180,522 lactation records of 94,558 Holstein cows from 937 Brazilian and 400 Colombian herds. Herds in both countries were jointly grouped in thirds according to 8 DVHE: production level, phenotypic variability, age at first calving, calving interval, percentage of imported semen, lactation length, and herd size. For each DVHE, REML bivariate animal model analyses were used to estimate genetic correlations for MY between upper and lower thirds of the data. Based on estimates of genetic correlations, weights were assigned to each DVHE to group herds in a cluster analysis using the FASTCLUS procedure in SAS. Three clusters were defined, and genetic and residual variance components were heterogeneous among herd clusters. Estimates of heritability in clusters 1 and 3 were 0.28 and 0.29, respectively, but the estimate was larger (0.39) in Cluster 2. The genetic correlations of MY from different clusters ranged from 0.89 to 0.97. The herd-cluster model based on DVHE properly takes into account G x E by grouping similar environments accordingly and seems to be an alternative to simply considering country borders to distinguish between environments.

Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study

PubMed Central

Bair, Eric; Gaynor, Sheila; Slade, Gary D.; Ohrbach, Richard; Fillingim, Roger B.; Greenspan, Joel D.; Dubner, Ronald; Smith, Shad B.; Diatchenko, Luda; Maixner, William

2016-01-01

The classification of most chronic pain disorders gives emphasis to anatomical location of the pain to distinguish one disorder from the other (eg, back pain vs temporomandibular disorder [TMD]) or to define subtypes (eg, TMD myalgia vs arthralgia). However, anatomical criteria overlook etiology, potentially hampering treatment decisions. This study identified clusters of individuals using a comprehensive array of biopsychosocial measures. Data were collected from a case–control study of 1031 chronic TMD cases and 3247 TMD-free controls. Three subgroups were identified using supervised cluster analysis (referred to as the adaptive, pain-sensitive, and global symptoms clusters). Compared with the adaptive cluster, participants in the pain-sensitive cluster showed heightened sensitivity to experimental pain, and participants in the global symptoms cluster showed both greater pain sensitivity and greater psychological distress. Cluster membership was strongly associated with chronic TMD: 91.5% of TMD cases belonged to the pain-sensitive and global symptoms clusters, whereas 41.2% of controls belonged to the adaptive cluster. Temporomandibular disorder cases in the pain-sensitive and global symptoms clusters also showed greater pain intensity, jaw functional limitation, and more comorbid pain conditions. Similar results were obtained when the same methodology was applied to a smaller case–control study consisting of 199 chronic TMD cases and 201 TMD-free controls. During a median 3-year follow-up period of TMD-free individuals, participants in the global symptoms cluster had greater risk of developing first-onset TMD (hazard ratio = 2.8) compared with participants in the other 2 clusters. Cross-cohort predictive modeling was used to demonstrate the reliability of the clusters. PMID:26928952

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Efficacy of GPS cluster analysis for predicting carnivory sites of a wide-ranging omnivore: the American black bear

USGS Publications Warehouse

Kindschuh, Sarah R.; Cain, James W.; Daniel, David; Peyton, Mark A.

2016-01-01

The capacity to describe and quantify predation by large carnivores expanded considerably with the advent of GPS technology. Analyzing clusters of GPS locations formed by carnivores facilitates the detection of predation events by identifying characteristics which distinguish predation sites. We present a performance assessment of GPS cluster analysis as applied to the predation and scavenging of an omnivore, the American black bear (Ursus americanus), on ungulate prey and carrion. Through field investigations of 6854 GPS locations from 24 individual bears, we identified 54 sites where black bears formed a cluster of locations while predating or scavenging elk (Cervus elaphus), mule deer (Odocoileus hemionus), or cattle (Bos spp.). We developed models for three data sets to predict whether a GPS cluster was formed at a carnivory site vs. a non-carnivory site (e.g., bed sites or non-ungulate foraging sites). Two full-season data sets contained GPS locations logged at either 3-h or 30-min intervals from April to November, and a third data set contained 30-min interval data from April through July corresponding to the calving period for elk. Longer fix intervals resulted in the detection of fewer carnivory sites. Clusters were more likely to be carnivory sites if they occurred in open or edge habitats, if they occurred in the early season, if the mean distance between all pairs of GPS locations within the cluster was less, and if the cluster endured for a longer period of time. Clusters were less likely to be carnivory sites if they were initiated in the morning or night compared to the day. The top models for each data set performed well and successfully predicted 71–96% of field-verified carnivory events, 55–75% of non–carnivory events, and 58–76% of clusters overall. Refinement of this method will benefit from further application across species and ecological systems.

Autism spectrum disorder in Down syndrome: cluster analysis of Aberrant Behaviour Checklist data supports diagnosis.

PubMed

Ji, N Y; Capone, G T; Kaufmann, W E

2011-11-01

The diagnostic validity of autism spectrum disorder (ASD) based on Diagnostic and Statistical Manual of Mental Disorders (DSM) has been challenged in Down syndrome (DS), because of the high prevalence of cognitive impairments in this population. Therefore, we attempted to validate DSM-based diagnoses via an unbiased categorisation of participants with a DSM-independent behavioural instrument. Based on scores on the Aberrant Behaviour Checklist - Community, we performed sequential factor (four DS-relevant factors: Autism-Like Behaviour, Disruptive Behaviour, Hyperactivity, Self-Injury) and cluster analyses on a 293-participant paediatric DS clinic cohort. The four resulting clusters were compared with DSM-delineated groups: DS + ASD, DS + None (no DSM diagnosis), DS + DBD (disruptive behaviour disorder) and DS + SMD (stereotypic movement disorder), the latter two as comparison groups. Two clusters were identified with DS + ASD: Cluster 1 (35.1%) with higher disruptive behaviour and Cluster 4 (48.2%) with more severe autistic behaviour and higher percentage of late onset ASD. The majority of participants in DS + None (71.9%) and DS + DBD (87.5%) were classified into Cluster 2 and 3, respectively, while participants in DS + SMD were relatively evenly distributed throughout the four clusters. Our unbiased, DSM-independent analyses, using a rating scale specifically designed for individuals with severe intellectual disability, demonstrated that DSM-based criteria of ASD are applicable to DS individuals despite their cognitive impairments. Two DS + ASD clusters were identified and supported the existence of at least two subtypes of ASD in DS, which deserve further characterisation. Despite the prominence of stereotypic behaviour in DS, the SMD diagnosis was not identified by cluster analysis, suggesting that high-level stereotypy is distributed throughout DS. Further supporting DSM diagnoses, typically behaving DS participants were easily distinguished as a group from those with maladaptive behaviours. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

Modelling the Kampungkota: A quantitative approach in defining Indonesian informal settlements

NASA Astrophysics Data System (ADS)

Anindito, D. B.; Maula, F. K.; Akbar, R.

2018-02-01

Bandung City is home to 2.5 million inhabitants, some of which are living in slums and squatter. However, the terms conveying this type of housing is not adequate to describe that of Indonesian called as kampungkota. Several studies suggest various variables in constituting kampungkota qualitatively. This study delves to define kampungkota in a quantitative manner, using the characteristics of slums and squatter. The samples for this study are 151 villages (kelurahan) in Bandung City. Ordinary Least Squares, Geographically Weighted Regression, and Spatial Cluster and Outlier Analysis are employed. It is suggested that kampungkota may have distinguished variables regarding to its location. As kampungkota may be smaller than administrative area of kelurahan, it can develop beyond the jurisdiction of kelurahan, as indicated by the clustering pattern of kampungkota.

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

PubMed

Takagiwa, Yoshiki; Kimura, Kaoru

2014-08-01

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

Phonologic errors as a clinical marker of the logopenic variant of PPA.

PubMed

Leyton, Cristian E; Ballard, Kirrie J; Piguet, Olivier; Hodges, John R

2014-05-06

To disentangle the clinical heterogeneity of nonsemantic variants of primary progressive aphasia (PPA) and to identify a coherent linguistic-anatomical marker for the logopenic variant of PPA (lv-PPA). Key speech and language features of 14 cases of lv-PPA and 18 cases of nonfluent/agrammatic variant of PPA were systematically evaluated and scored by an independent rater blinded to diagnosis. Every case underwent a structural MRI and a Pittsburgh compound B (PiB)-PET scan, a putative biomarker of Alzheimer disease. Key speech and language features that showed association with the PiB-PET status were entered into a hierarchical cluster analysis. The linguistic features and patterns of cortical thinning in each resultant cluster were analyzed. The cluster analysis revealed 3 coherent clinical groups, each of which was linked to a specific PiB-PET status. The first cluster was linked to high PiB retention and characterized by phonologic errors and cortical thinning focused on the left superior temporal gyrus. The second and third clusters were characterized by grammatical production errors and motor speech disorders, respectively, and were associated with low PiB brain retention. A fourth cluster, however, demonstrated nonspecific language deficits and unpredictable PiB-PET status. These findings suggest that despite the clinical and pathologic heterogeneity of nonsemantic variants, discrete clinical syndromes can be distinguished and linked to specific likelihood of PiB-PET status. Phonologic errors seem to be highly predictive of high amyloid burden in PPA and can provide a specific clinical marker for lv-PPA.

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.

PubMed

Hsu, Arthur L; Tang, Sen-Lin; Halgamuge, Saman K

2003-11-01

Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class discovery, the proposed algorithm identifies corresponding sets of predictor genes that best distinguish one class from other classes. The approach integrates merits of hierarchical clustering with robustness against noise known from self-organizing approaches. The proposed algorithm applied to DNA microarray data sets of two types of cancers has demonstrated its ability to produce the most suitable number of clusters. Further, the corresponding marker genes identified through the unsupervised algorithm also have a strong biological relationship to the specific cancer class. The algorithm tested on leukemia microarray data, which contains three leukemia types, was able to determine three major and one minor cluster. Prediction models built for the four clusters indicate that the prediction strength for the smaller cluster is generally low, therefore labelled as uncertain cluster. Further analysis shows that the uncertain cluster can be subdivided further, and the subdivisions are related to two of the original clusters. Another test performed using colon cancer microarray data has automatically derived two clusters, which is consistent with the number of classes in data (cancerous and normal). JAVA software of dynamic SOM tree algorithm is available upon request for academic use. A comparison of rectangular and hexagonal topologies for GSOM is available from http://www.mame.mu.oz.au/mechatronics/journalinfo/Hsu2003supp.pdf

Determinants of the use of dietary supplements among secondary and high school students

PubMed

Gajda, Karolina; Zielińska, Monika; Ciecierska, Anna; Hamułka, Jadwiga

All over the world, including Poland, the sale of dietary supplements is increasing. More and more often, people including children and youths, use dietary supplements on their own initiative and without any medical indications or knowledge in this field. Analysis of the conditions of using the dietary supplements with vitamins and minerals among secondary school and high school students in Poland. The study included 396 students aged 13-18 years (249 girls and 147 boys). Authors’ questionnaire was used to evaluate the intake of dietary supplements. The use of cluster analysis allowed to distinguish groups of students with similar socio-demographic characteristics and the frequency of use of dietary supplements. In the studied population of students three clusters were created that significantly differed in socio-demographic characteristics. In cluster 1 and 2, were mostly students who used dietary supplements (respectively, 56% of respondents and 100%). In cluster 1 there were mostly students coming from rural areas and small city, with a worse financial situation, mainly boys (56%), while cluster 2 was dominated by girls (81%) living in a big city, coming from families with a good financial situation and who were more likely to be underweight (28.8%). In cluster 3 there were mostly older students (62%), not taking dietary supplements. In comparison to cluster 2, they had lower frequency of breakfast consumption (55% vs. 69%), but higher frequency of the consumption of soft drinks, fast-food, coffee as well as salt use at the table. The results show that the use of dietary supplements in adolescence is a common phenomenon and slightly conditioned by eating behaviors. This unfavorable habit of common dietary supplements intake observed among students indicates the need for education on the benefits and risks of the supplements usage.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

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 the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.

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 by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences. PMID:28187133

Preinjury coping, emotional functioning, and quality of life following uncomplicated and complicated mild traumatic brain injury.

PubMed

Maestas, Kacey Little; Sander, Angelle M; Clark, Allison N; van Veldhoven, Laura M; Struchen, Margaret A; Sherer, Mark; Hannay, H Julia

2014-01-01

To identify preinjury coping profiles among adults with uncomplicated mild traumatic brain injury (mTBI) and complicated mTBI and to determine whether preinjury coping profiles contribute to the prediction of emotional functioning and quality of life (QOL) 3 months post-mTBI. One hundred eighty-seven persons with medically documented mTBI (uncomplicated mTBI, n = 89; complicated mTBI, n = 98) were recruited from the emergency center of a level I trauma center and followed in community 3 months post-mTBI. The Ways of Coping Questionnaire was administered within 2 weeks of injury. Cluster analysis was used to group participants on basis of their preinjury use of problem-focused and avoidant coping strategies. The Brief Symptom Inventory and the 36-item Short-Form Health Survey were administered 3 months postinjury. Cluster analysis distinguished 3 distinct preinjury coping profiles that were differentially associated with outcomes. Participants who used avoidant coping showed the worse emotional functioning and QOL outcomes, although this cluster also reported high usage of problem-focused strategies. Preinjury coping profiles explained a significant proportion of the variance in depression, anxiety, and mental health QOL at 3 months postinjury beyond that accounted for by demographic characteristics and mTBI severity. Cluster analysis holds practical value in illustrating the pattern of coping strategies used by person with uncomplicated and complicated mTBI. It appears worthwhile to address coping in future trials of interventions that are aimed at improving emotional functioning after mTBI.

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

PubMed

Han, Bangxing; Peng, Huasheng; Yan, Hui

2016-01-01

Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

DNA Encapsulation of Ten Silver Atoms Produces a Bright, Modulatable, Near Infrared-Emitting Cluster

PubMed Central

Petty, Jeffrey T.; Fan, Chaoyang; Story, Sandra P.; Sengupta, Bidisha; Iyer, Ashlee St. John; Prudowsky, Zachary; Dickson, Robert M.

2010-01-01

Photostability, inherent fluorescence brightness, and optical modulation of fluorescence are key attributes distinguishing silver nanoclusters as fluorophores. DNA plays a central role both by protecting the clusters in aqueous environments and by directing their formation. Herein, we characterize a new near infrared-emitting cluster with excitation and emission maxima at 750 and 810 nm, respectively that is stabilized within C3AC3AC3TC3A. Following chromatographic resolution of the near infrared species, a stoichiometry of 10 Ag/oligonucleotide was determined. Combined with excellent photostability, the cluster’s 30% fluorescence quantum yield and 180,000 M−1cm−1 extinction coefficient give it a fluorescence brightness that significantly improves on that of the organic dye Cy7. Fluorescence correlation analysis shows an optically accessible dark state that can be directly depopulated with longer wavelength co-illumination. The coupled increase in total fluorescence demonstrates that enhanced sensitivity can be realized through Synchronously Amplified Fluorescence Image Recovery (SAFIRe), which further differentiates this new fluorophore. PMID:21116486

New Techniques for Ancient Proteins: Direct Coupling Analysis Applied on Proteins Involved in Iron Sulfur Cluster Biogenesis

PubMed Central

Fantini, Marco; Malinverni, Duccio; De Los Rios, Paolo; Pastore, Annalisa

2017-01-01

Direct coupling analysis (DCA) is a powerful statistical inference tool used to study protein evolution. It was introduced to predict protein folds and protein-protein interactions, and has also been applied to the prediction of entire interactomes. Here, we have used it to analyze three proteins of the iron-sulfur biogenesis machine, an essential metabolic pathway conserved in all organisms. We show that DCA can correctly reproduce structural features of the CyaY/frataxin family (a protein involved in the human disease Friedreich's ataxia) despite being based on the relatively small number of sequences allowed by its genomic distribution. This result gives us confidence in the method. Its application to the iron-sulfur cluster scaffold protein IscU, which has been suggested to function both as an ordered and a disordered form, allows us to distinguish evolutionary traces of the structured species, suggesting that, if present in the cell, the disordered form has not left evolutionary imprinting. We observe instead, for the first time, direct indications of how the protein can dimerize head-to-head and bind 4Fe4S clusters. Analysis of the alternative scaffold protein IscA provides strong support to a coordination of the cluster by a dimeric form rather than a tetramer, as previously suggested. Our analysis also suggests the presence in solution of a mixture of monomeric and dimeric species, and guides us to the prevalent one. Finally, we used DCA to analyze interactions between some of these proteins, and discuss the potentials and limitations of the method. PMID:28664160

Nonlinearities in the Evolutional Distinctions Between El Niño and La Niña Types

NASA Astrophysics Data System (ADS)

Ashok, K.; Shamal, M.; Sahai, A. K.; Swapna, P.

2017-12-01

Using the HadISST, SODA reanalysis, and various other observed and reanalyzed data sets for the period 1950-2010, we explore nonlinearities in the subsurface evolutional distinctions between El Niño types and La Niña types from a few seasons before the onset. Cluster analysis carried out over both summer and winter suggests that while the warm-phased events of both types are distinguishable, several cold phased events are clustered together. Further, we apply a joint Self-Organizing Map (SOM) analysis using the monthly sea surface temperature anomaly (SSTA) and thermocline-depth anomalies in tropical Pacific (TP). Results reveal that the evolutionary paths of El Niño Modoki (EM) and El Niño (EL) are, broadly, different. Subsurface temperature composites of EL and EM show different onset characteristics. During an EL, warm anomaly in the west spreads eastward along the thermocline and reaches the surface in the east in March-May of year(0). During an EM, warm anomaly already exists in the central tropical Pacific and then reaches the surface in the east in September-November of year(0). Composited SSTAs during La Niña (LN) and La Niña Modoki (LM) are distinguishable only at 80% confidence level, but the composited subsurface temperature anomalies show differences in the location of the coldest anomaly as well as evolution at 90% confidence level. Thus, the El Niño flavor distinction is potentially predictable at longer leads.

Cultivar identification and genetic relationship of pineapple (Ananas comosus) cultivars using SSR markers.

PubMed

Lin, Y S; Kuan, C S; Weng, I S; Tsai, C C

2015-11-25

The genetic relationships among 27 pineapple [Ananas comosus (L.) Merr.] cultivars and lines were examined using 16 simple sequence repeat (SSR) markers. The number of alleles per locus of the SSR markers ranged from 2 to 6 (average 3.19), for a total of 51 alleles. Similarity coefficients were calculated on the basis of 51 amplified bands. A dendrogram was created according to the 16 SSR markers by the unweighted pair-group method. The banding patterns obtained from the SSR primers allowed most of the cultivars and lines to be distinguished, with the exception of vegetative clones. According to the dendrogram, the 27 pineapple cultivars and lines were clustered into three main clusters and four individual clusters. As expected, the dendrogram showed that derived cultivars and lines are closely related to their parental cultivars; the genetic relationships between pineapple cultivars agree with the genealogy of their breeding history. In addition, the analysis showed that there is no obvious correlation between SSR markers and morphological characters. In conclusion, SSR analysis is an efficient method for pineapple cultivar identification and can offer valuable informative characters to identify pineapple cultivars in Taiwan.

Phylogenetic Distribution of the Capsid Assembly Protein Gene (g20) of Cyanophages in Paddy Floodwaters in Northeast China

PubMed Central

Jing, Ruiyong; Liu, Junjie; Yu, Zhenhua; Liu, Xiaobing; Wang, Guanghua

2014-01-01

Numerous studies have revealed the high diversity of cyanophages in marine and freshwater environments, but little is currently known about the diversity of cyanophages in paddy fields, particularly in Northeast (NE) China. To elucidate the genetic diversity of cyanophages in paddy floodwaters in NE China, viral capsid assembly protein gene (g20) sequences from five floodwater samples were amplified with the primers CPS1 and CPS8. Denaturing gradient gel electrophoresis (DGGE) was applied to distinguish different g20 clones. In total, 54 clones differing in g20 nucleotide sequences were obtained in this study. Phylogenetic analysis showed that the distribution of g20 sequences in this study was different from that in Japanese paddy fields, and all the sequences were grouped into Clusters α, β, γ and ε. Within Clusters α and β, three new small clusters (PFW-VII∼-IX) were identified. UniFrac analysis of g20 clone assemblages demonstrated that the community compositions of cyanophage varied among marine, lake and paddy field environments. In paddy floodwater, community compositions of cyanophage were also different between NE China and Japan. PMID:24533125

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2010-04-01

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

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides.

PubMed

Melo, Armindo; Pinto, Edgar; Aguiar, Ana; Mansilha, Catarina; Pinho, Olívia; Ferreira, Isabel M P L V O

2012-07-01

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.

Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

PubMed Central

Goodpaster, Aaron M.; Kennedy, Michael A.

2015-01-01

Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T2 test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data. PMID:26246647

Molecular classification of gastric cancer: a new paradigm.

PubMed

Shah, Manish A; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y; Klimstra, David S; Gerdes, Hans; Kelsen, David P

2011-05-01

Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. ©2011 AACR.

Molecular Classification of Gastric Cancer: A new paradigm

PubMed Central

Shah, Manish A.; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y.; Klimstra, David S.; Gerdes, Hans; Kelsen, David P.

2011-01-01

Purpose Gastric cancer may be subdivided into three distinct subtypes –proximal, diffuse, and distal gastric cancer– based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Experimental Design Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (NCI 5917) underwent endoscopic biopsy for fresh tumor procurement. 4–6 targeted biopsies of the primary tumor were obtained. Macrodissection was performed to ensure >80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Results Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the three gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross validation error was 0.14, suggesting that >85% of samples were classified correctly. Gene set analysis with the False Discovery Rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Conclusions Subtypes of gastric cancer that have epidemiologic and histologic distinction are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. PMID:21430069

Geotemporal Analysis of Neisseria meningitidis Clones in the United States: 2000–2005

PubMed Central

Wiringa, Ann E.; Shutt, Kathleen A.; Marsh, Jane W.; Cohn, Amanda C.; Messonnier, Nancy E.; Zansky, Shelley M.; Petit, Susan; Farley, Monica M.; Gershman, Ken; Lynfield, Ruth; Reingold, Arthur; Schaffner, William; Thompson, Jamie; Brown, Shawn T.; Lee, Bruce Y.; Harrison, Lee H.

2013-01-01

Background The detection of meningococcal outbreaks relies on serogrouping and epidemiologic definitions. Advances in molecular epidemiology have improved the ability to distinguish unique Neisseria meningitidis strains, enabling the classification of isolates into clones. Around 98% of meningococcal cases in the United States are believed to be sporadic. Methods Meningococcal isolates from 9 Active Bacterial Core surveillance sites throughout the United States from 2000 through 2005 were classified according to serogroup, multilocus sequence typing, and outer membrane protein (porA, porB, and fetA) genotyping. Clones were defined as isolates that were indistinguishable according to this characterization. Case data were aggregated to the census tract level and all non-singleton clones were assessed for non-random spatial and temporal clustering using retrospective space-time analyses with a discrete Poisson probability model. Results Among 1,062 geocoded cases with available isolates, 438 unique clones were identified, 78 of which had ≥2 isolates. 702 cases were attributable to non-singleton clones, accounting for 66.0% of all geocoded cases. 32 statistically significant clusters comprised of 107 cases (10.1% of all geocoded cases) were identified. Clusters had the following attributes: included 2 to 11 cases; 1 day to 33 months duration; radius of 0 to 61.7 km; and attack rate of 0.7 to 57.8 cases per 100,000 population. Serogroups represented among the clusters were: B (n = 12 clusters, 45 cases), C (n = 11 clusters, 27 cases), and Y (n = 9 clusters, 35 cases); 20 clusters (62.5%) were caused by serogroups represented in meningococcal vaccines that are commercially available in the United States. Conclusions Around 10% of meningococcal disease cases in the U.S. could be assigned to a geotemporal cluster. Molecular characterization of isolates, combined with geotemporal analysis, is a useful tool for understanding the spread of virulent meningococcal clones and patterns of transmission in populations. PMID:24349182

UV TO FAR-IR CATALOG OF A GALAXY SAMPLE IN NEARBY CLUSTERS: SPECTRAL ENERGY DISTRIBUTIONS AND ENVIRONMENTAL TRENDS

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

Hernandez-Fernandez, Jonathan D.; Iglesias-Paramo, J.; Vilchez, J. M., E-mail: jonatan@iaa.es

2012-03-01

In this paper, we present a sample of cluster galaxies devoted to study the environmental influence on the star formation activity. This sample of galaxies inhabits in clusters showing a rich variety in their characteristics and have been observed by the SDSS-DR6 down to M{sub B} {approx} -18, and by the Galaxy Evolution Explorer AIS throughout sky regions corresponding to several megaparsecs. We assign the broadband and emission-line fluxes from ultraviolet to far-infrared to each galaxy performing an accurate spectral energy distribution for spectral fitting analysis. The clusters follow the general X-ray luminosity versus velocity dispersion trend of L{sub X}more » {proportional_to} {sigma}{sup 4.4}{sub c}. The analysis of the distributions of galaxy density counting up to the 5th nearest neighbor {Sigma}{sub 5} shows: (1) the virial regions and the cluster outskirts share a common range in the high density part of the distribution. This can be attributed to the presence of massive galaxy structures in the surroundings of virial regions. (2) The virial regions of massive clusters ({sigma}{sub c} > 550 km s{sup -1}) present a {Sigma}{sub 5} distribution statistically distinguishable ({approx}96%) from the corresponding distribution of low-mass clusters ({sigma}{sub c} < 550 km s{sup -1}). Both massive and low-mass clusters follow a similar density-radius trend, but the low-mass clusters avoid the high density extreme. We illustrate, with ABELL 1185, the environmental trends of galaxy populations. Maps of sky projected galaxy density show how low-luminosity star-forming galaxies appear distributed along more spread structures than their giant counterparts, whereas low-luminosity passive galaxies avoid the low-density environment. Giant passive and star-forming galaxies share rather similar sky regions with passive galaxies exhibiting more concentrated distributions.« less

Genetic Markers Analyses and Bioinformatic Approaches to Distinguish Between Olive Tree (Olea europaea L.) Cultivars.

PubMed

Ben Ayed, Rayda; Ben Hassen, Hanen; Ennouri, Karim; Rebai, Ahmed

2016-12-01

The genetic diversity of 22 olive tree cultivars (Olea europaea L.) sampled from different Mediterranean countries was assessed using 5 SNP markers (FAD2.1; FAD2.3; CALC; SOD and ANTHO3) located in four different genes. The genotyping analysis of the 22 cultivars with 5 SNP loci revealed 11 alleles (average 2.2 per allele). The dendrogram based on cultivar genotypes revealed three clusters consistent with the cultivars classification. Besides, the results obtained with the five SNPs were compared to those obtained with the SSR markers using bioinformatic analyses and by computing a cophenetic correlation coefficient, indicating the usefulness of the UPGMA method for clustering plant genotypes. Based on principal coordinate analysis using a similarity matrix, the first two coordinates, revealed 54.94 % of the total variance. This work provides a more comprehensive explanation of the diversity available in Tunisia olive cultivars, and an important contribution for olive breeding and olive oil authenticity.

Cluster-distinguishing genotypic and phenotypic diversity of carbapenem-resistant Gram-negative bacteria in solid-organ transplantation patients: a comparative study.

PubMed

Karampatakis, Theodoros; Geladari, Anastasia; Politi, Lida; Antachopoulos, Charalampos; Iosifidis, Elias; Tsiatsiou, Olga; Karyoti, Aggeliki; Papanikolaou, Vasileios; Tsakris, Athanassios; Roilides, Emmanuel

2017-07-31

Solid-organ transplant recipients may display high rates of colonization and/or infection by multidrug-resistant bacteria. We analysed and compared the phenotypic and genotypic diversity of carbapenem-resistant (CR) strains of Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii isolated from patients in the Solid Organ Transplantation department of our hospital. Between March 2012 and August 2013, 56 CR strains from various biological fluids underwent antimicrobial susceptibility testing with VITEK 2, molecular analysis by PCR amplification and genotypic analysis with pulsed-field gel electrophoresis (PFGE). They were clustered according to antimicrobial drug susceptibility and genotypic profiles. Diversity analyses were performed by calculating Simpson's diversity index and applying computed rarefaction curves.Results/Key findings. Among K. pneumoniae, KP-producers predominated (57.1 %). VIM and OXA-23 carbapenemases prevailed among P. aeruginosa and A. baumannii (89.4 and 88.9 %, respectively). KPC-producing K. pneumoniae and OXA-23 A. baumannii were assigned in single PFGE pulsotypes. VIM-producing P. aeruginosa generated multiple pulsotypes. CR K. pneumoniae strains displayed phenotypic diversity in tigecycline, colistin (CS), amikacin (AMK), gentamicin (GEN) and co-trimoxazole (SXT) (16 clusters); P. aeruginosa displayed phenotypic diversity in cefepime (FEP), ceftazidime, aztreonam, piperacillin, piperacillin-tazobactam, AMK, GEN and CS (9 clusters); and A. baumannii displayed phenotypic diversity in AMK, GEN, SXT, FEP, tobramycin and rifampicin (8 clusters). The Simpson diversity indices for the interpretative phenotype and PFGE analysis were 0.89 and 0.6, respectively, for K. pneumoniae strains (P<0.001); 0.77 and 0.6 for P. aeruginosa (P=0.22); and 0.86 and 0.19 for A. baumannii (P=0.004). The presence of different antimicrobial susceptibility profiles does not preclude the possibility that two CR K. pneumoniae or A. baumannii isolates are clonally related.

Using an index of habitat patch proximity for landscape design

Treesearch

Eric J. Gustafson; George R. Parker

1994-01-01

A proximity index (PX) inspired by island biogeography theory is described which quantifies the spatial context of a habitat patch in relation to its neighbors. The index distinguishes sparse distributions of small habitat patches from clusters of large patches. An evaluation of the relationship between PX and variation in the spatial characteristics of clusters of...

Population Structure of Two Rabies Hosts Relative to the Known Distribution of Rabies Virus Variants in Alaska

PubMed Central

Goldsmith, Elizabeth W.; Renshaw, Benjamin; Clement, Christopher J.; Himschoot, Elizabeth A.; Hundertmark, Kris J.; Hueffer, Karsten

2015-01-01

For pathogens that infect multiple species the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We test the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (V. vulpes) in order to possibly distinguish reservoir and spill over hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found 2 groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising 2 regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the artic fox Therefore we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches. PMID:26661691

Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska.

PubMed

Goldsmith, Elizabeth W; Renshaw, Benjamin; Clement, Christopher J; Himschoot, Elizabeth A; Hundertmark, Kris J; Hueffer, Karsten

2016-02-01

For pathogens that infect multiple species, the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We tested the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (Vulpes vulpes) to possibly distinguish reservoir and spillover hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found two groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising two regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the arctic fox. Therefore, we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches. © 2015 John Wiley & Sons Ltd.

Cluster stability in the analysis of mass cytometry data.

PubMed

Melchiotti, Rossella; Gracio, Filipe; Kordasti, Shahram; Todd, Alan K; de Rinaldis, Emanuele

2017-01-01

Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and contributes to the construction of a more systematic and standardized analytical framework for the assessment of cytometry clustering results. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

COOL CORE CLUSTERS FROM COSMOLOGICAL SIMULATIONS

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

Rasia, E.; Borgani, S.; Murante, G.

2015-11-01

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and active galactic nucleus (AGN) feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and themore » iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of CC systems, to nearly flat core isentropic profiles, characteristic of NCC systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and observations. Furthermore, we also find that simulated CC clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic CC structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.« less

Cool Core Clusters from Cosmological Simulations

NASA Astrophysics Data System (ADS)

Rasia, E.; Borgani, S.; Murante, G.; Planelles, S.; Beck, A. M.; Biffi, V.; Ragone-Figueroa, C.; Granato, G. L.; Steinborn, L. K.; Dolag, K.

2015-11-01

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and active galactic nucleus (AGN) feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of CC systems, to nearly flat core isentropic profiles, characteristic of NCC systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and observations. Furthermore, we also find that simulated CC clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic CC structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.

Automated modal parameter estimation using correlation analysis and bootstrap sampling

NASA Astrophysics Data System (ADS)

Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.

2018-02-01

The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

A classification of substance-dependent men on temperament and severity variables.

PubMed

Henderson, Melinda J; Galen, Luke W

2003-06-01

This study examined the validity of classifying substance abusers based on temperament and dependence severity, and expanded the scope of typology differences to proximal determinants of use (e.g., expectancies, motives). Patients were interviewed about substance use, depression, and family history of alcohol and drug abuse. Self-report instruments measuring temperament, expectancies, and motives were completed. Participants were 147 male veterans admitted to inpatient substance abuse treatment at a U.S. Department of Veterans Affairs medical center. Cluster analysis identified four types of users with two high substance problem severity and two low substance problem severity groups. Two, high problem severity, early onset groups differed only on the cluster variable of negative affectivity (NA), but showed differences on antisocial personality characteristics, hypochondriasis, and coping motives for alcohol. The two low problem severity groups were distinguished by age of onset and positive affectivity (PA). The late onset, low PA group had a higher incidence of depression, a greater tendency to use substances in solitary contexts, and lower enhancement motives for alcohol compared to the early onset, high PA cluster. The four-cluster solution yielded more distinctions on external criteria than the two-cluster solution. Such temperament variation within both high and low severity substance abusers may be important for treatment planning.

Skill transfer, affordances and dexterity in different climbing environments.

PubMed

Seifert, L; Wattebled, L; L'hermette, M; Bideault, G; Herault, R; Davids, K

2013-12-01

This study explored how skills in one region of a perceptual-motor landscape of performance, created in part by previous experience in rock climbing, can shape those that emerge in another region (ice climbing). Ten novices in rock climbing and five intermediate rock climbers were observed climbing an icefall. Locations of right and left ice tools and crampons were videotaped from a frontal camera. Inter-individual variability of upper and lower limb couplings and types of action regarding icefall properties were assessed by cluster hierarchical analysis, distinguishing three clusters. Pelvis vertical displacement, duration and number of pelvis pauses were also analyzed. Experienced rock climbers were grouped in the same cluster and showed the highest range and variability of limb angular locations and coordination patterns, the highest vertical displacement and the shortest pelvis plateaux durations. Non-fluent climbers (clusters 2 and 3) showed low range and variability of limb angular locations and coordination patterns. In particular, climbers of cluster 3 exhibited the lowest vertical displacement, the longest plateaux durations and the greatest ratio between tool swinging and definitive anchorage. Our results exemplified the positive influence of skills in rock climbing on ice climbing performance, facilitated by the detection of affordances from environmental properties. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatial dynamics of invasion: the geometry of introduced species.

PubMed

Korniss, Gyorgy; Caraco, Thomas

2005-03-07

Many exotic species combine low probability of establishment at each introduction with rapid population growth once introduction does succeed. To analyse this phenomenon, we note that invaders often cluster spatially when rare, and consequently an introduced exotic's population dynamics should depend on locally structured interactions. Ecological theory for spatially structured invasion relies on deterministic approximations, and determinism does not address the observed uncertainty of the exotic-introduction process. We take a new approach to the population dynamics of invasion and, by extension, to the general question of invasibility in any spatial ecology. We apply the physical theory for nucleation of spatial systems to a lattice-based model of competition between plant species, a resident and an invader, and the analysis reaches conclusions that differ qualitatively from the standard ecological theories. Nucleation theory distinguishes between dynamics of single- and multi-cluster invasion. Low introduction rates and small system size produce single-cluster dynamics, where success or failure of introduction is inherently stochastic. Single-cluster invasion occurs only if the cluster reaches a critical size, typically preceded by a number of failed attempts. For this case, we identify the functional form of the probability distribution of time elapsing until invasion succeeds. Although multi-cluster invasion for sufficiently large systems exhibits spatial averaging and almost-deterministic dynamics of the global densities, an analytical approximation from nucleation theory, known as Avrami's law, describes our simulation results far better than standard ecological approximations.

Metagenomic Analyses Reveal That Energy Transfer Gene Abundances Can Predict the Syntrophic Potential of Environmental Microbial Communities.

PubMed

Oberding, Lisa; Gieg, Lisa M

2016-01-05

Hydrocarbon compounds can be biodegraded by anaerobic microorganisms to form methane through an energetically interdependent metabolic process known as syntrophy. The microorganisms that perform this process as well as the energy transfer mechanisms involved are difficult to study and thus are still poorly understood, especially on an environmental scale. Here, metagenomic data was analyzed for specific clusters of orthologous groups (COGs) related to key energy transfer genes thus far identified in syntrophic bacteria, and principal component analysis was used in order to determine whether potentially syntrophic environments could be distinguished using these syntroph related COGs as opposed to universally present COGs. We found that COGs related to hydrogenase and formate dehydrogenase genes were able to distinguish known syntrophic consortia and environments with the potential for syntrophy from non-syntrophic environments, indicating that these COGs could be used as a tool to identify syntrophic hydrocarbon biodegrading environments using metagenomic data.

Distribution of phenolic compounds and antioxidant capacity in apples tissues during ripening.

PubMed

Alberti, Aline; Zielinski, Acácio Antonio Ferreira; Couto, Marcelo; Judacewski, Priscila; Mafra, Luciana Igarashi; Nogueira, Alessandro

2017-05-01

The effect of variety and ripening stage on the distribution of phenolic compounds and in vitro antioxidant capacity of Gala, Fuji Suprema and Eva apples were evaluated. Hydroxycinnamic acids, flavonoids, flavanols, flavonols, dihydrochalcones and antioxidant activity (FRAP and DPPH) were assessed in the epicarp, mesocarp and endocarp of three varieties at three ripening stages (unripe, ripe and senescent). The Fuji Suprema variety distinguished by its content of flavonols at senescent stage, while Eva variety distinguished by its content of dihydrochalcones (unripe stage) and anthocyanins (ripe stage). In general, phenolic acids and flavonoids decreased with ripening in the epicarp and endocarp. However, in the mesocarp, the effect of ripening was related with the apple variety. Hierarchical cluster analysis confirmed the influence of ripening in the apple tissue. The evolution of these compounds during ripening occurred irregularly and it was influenced by the variety.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

PubMed

Lakhman, Yulia; Veeraraghavan, Harini; Chaim, Joshua; Feier, Diana; Goldman, Debra A; Moskowitz, Chaya S; Nougaret, Stephanie; Sosa, Ramon E; Vargas, Hebert Alberto; Soslow, Robert A; Abu-Rustum, Nadeem R; Hricak, Hedvig; Sala, Evis

2017-07-01

To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.

Clinical, epidemiological, and spatial characteristics of Vibrio parahaemolyticus diarrhea and cholera in the urban slums of Kolkata, India

PubMed Central

2012-01-01

Background There is not much information on the differences in clinical, epidemiological and spatial characteristics of diarrhea due to V. cholerae and V. parahaemolyticus from non-coastal areas. We investigated the differences in clinical, epidemiological and spatial characteristics of the two Vibrio species in the urban slums of Kolkata, India. Methods The data of a cluster randomized cholera vaccine trial were used. We restricted the analysis to clusters assigned to placebo. Survival analysis of the time to the first episode was used to analyze risk factors for V. parahaemolyticus diarrhea or cholera. A spatial scan test was used to identify high risk areas for cholera and for V. parahaemolyticus diarrhea. Results In total, 54,519 people from the placebo clusters were assembled. The incidence of cholera (1.30/1000/year) was significantly higher than that of V. parahaemolyticus diarrhea (0.63/1000/year). Cholera incidence was inversely related to age, whereas the risk of V. parahaemolyticus diarrhea was age-independent. The seasonality of diarrhea due to the two Vibrio species was similar. Cholera was distinguished by a higher frequency of severe dehydration, and V. parahaemolyticus diarrhea was by abdominal pain. Hindus and those who live in household not using boiled or treated water were more likely to have V. parahaemolyticus diarrhea. Young age, low socioeconomic status, and living closer to a project healthcare facility were associated with an increased risk for cholera. The high risk area for cholera differed from the high risk area for V. parahaemolyticus diarrhea. Conclusion We report coexistence of the two vibrios in the slums of Kolkata. The two etiologies of diarrhea had a similar seasonality but had distinguishing clinical features. The risk factors and the high risk areas for the two diseases differ from one another suggesting different modes of transmission of these two pathogens. PMID:23020794

[Health status, use of health services and reported morbidity: application of correspondence analysis].

PubMed

Espinàs, J A; Riba, M D; Borràs, J M; Sánchez, V

1995-01-01

The study of the relationship between self-reported morbidity, health status and health care utilization presents methodological problems due to the variety of illnesses and medical conditions that one individual may report. In this article, correspondence analysis was use to analyse these relationships. Data from the Spanish National Health Survey pertaining to the region of Catalonia was studied. Statistical analysis included multi-way correspondence analysis (MCA) followed by cluster analysis. The first factor extracted is defined by self-assessed health perception; the second, by limitation of activities, and the third is related to self-reported morbidity caused by chronic and acute health problems. Fourth and fifth factors, capture residual variability and missing values. Acute problems are more related to perception of poor health while chronic problems are related to perception of fair health. Also, it may be possible to distinguish self-reported morbidity due to relapses of chronic diseases from true acute health problems. Cluster analysis classified individuals into four groups: 1) healthy people; 2) people who assess their health as being poor and those with acute health problems; 3) people with chronic health problems, limited activity and a perception of fair health; and 4) missing values. Correspondence analysis is a useful tool when analyzing qualitative variables like those in a health survey.

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

PubMed

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

1997-08-01

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

Assessing the genome level diversity of Listeria monocytogenes from contaminated ice cream and environmental samples linked to a listeriosis outbreak in the United States.

PubMed

Chen, Yi; Luo, Yan; Curry, Phillip; Timme, Ruth; Melka, David; Doyle, Matthew; Parish, Mickey; Hammack, Thomas S; Allard, Marc W; Brown, Eric W; Strain, Errol A

2017-01-01

A listeriosis outbreak in the United States implicated contaminated ice cream produced by one company, which operated 3 facilities. We performed single nucleotide polymorphism (SNP)-based whole genome sequencing (WGS) analysis on Listeria monocytogenes from food, environmental and clinical sources, identifying two clusters and a single branch, belonging to PCR serogroup IIb and genetic lineage I. WGS Cluster I, representing one outbreak strain, contained 82 food and environmental isolates from Facility I and 4 clinical isolates. These isolates differed by up to 29 SNPs, exhibited 9 pulsed-field gel electrophoresis (PFGE) profiles and multilocus sequence typing (MLST) sequence type (ST) 5 of clonal complex 5 (CC5). WGS Cluster II contained 51 food and environmental isolates from Facility II, 4 food isolates from Facility I and 5 clinical isolates. Among them the isolates from Facility II and clinical isolates formed a clade and represented another outbreak strain. Isolates in this clade differed by up to 29 SNPs, exhibited 3 PFGE profiles and ST5. The only isolate collected from Facility III belonged to singleton ST489, which was in a single branch separate from Clusters I and II, and was not associated with the outbreak. WGS analyses clustered together outbreak-associated isolates exhibiting multiple PFGE profiles, while differentiating them from epidemiologically unrelated isolates that exhibited outbreak PFGE profiles. The complete genome of a Cluster I isolate allowed the identification and analyses of putative prophages, revealing that Cluster I isolates differed by the gain or loss of three putative prophages, causing the banding pattern differences among all 3 AscI-PFGE profiles observed in Cluster I isolates. WGS data suggested that certain ice cream varieties and/or production lines might have contamination sources unique to them. The SNP-based analysis was able to distinguish CC5 as a group from non-CC5 isolates and differentiate among CC5 isolates from different outbreaks/incidents.

Assessing the genome level diversity of Listeria monocytogenes from contaminated ice cream and environmental samples linked to a listeriosis outbreak in the United States

PubMed Central

Chen, Yi; Luo, Yan; Curry, Phillip; Timme, Ruth; Melka, David; Doyle, Matthew; Parish, Mickey; Hammack, Thomas S.; Allard, Marc W.; Brown, Eric W.; Strain, Errol A.

2017-01-01

A listeriosis outbreak in the United States implicated contaminated ice cream produced by one company, which operated 3 facilities. We performed single nucleotide polymorphism (SNP)-based whole genome sequencing (WGS) analysis on Listeria monocytogenes from food, environmental and clinical sources, identifying two clusters and a single branch, belonging to PCR serogroup IIb and genetic lineage I. WGS Cluster I, representing one outbreak strain, contained 82 food and environmental isolates from Facility I and 4 clinical isolates. These isolates differed by up to 29 SNPs, exhibited 9 pulsed-field gel electrophoresis (PFGE) profiles and multilocus sequence typing (MLST) sequence type (ST) 5 of clonal complex 5 (CC5). WGS Cluster II contained 51 food and environmental isolates from Facility II, 4 food isolates from Facility I and 5 clinical isolates. Among them the isolates from Facility II and clinical isolates formed a clade and represented another outbreak strain. Isolates in this clade differed by up to 29 SNPs, exhibited 3 PFGE profiles and ST5. The only isolate collected from Facility III belonged to singleton ST489, which was in a single branch separate from Clusters I and II, and was not associated with the outbreak. WGS analyses clustered together outbreak-associated isolates exhibiting multiple PFGE profiles, while differentiating them from epidemiologically unrelated isolates that exhibited outbreak PFGE profiles. The complete genome of a Cluster I isolate allowed the identification and analyses of putative prophages, revealing that Cluster I isolates differed by the gain or loss of three putative prophages, causing the banding pattern differences among all 3 AscI-PFGE profiles observed in Cluster I isolates. WGS data suggested that certain ice cream varieties and/or production lines might have contamination sources unique to them. The SNP-based analysis was able to distinguish CC5 as a group from non-CC5 isolates and differentiate among CC5 isolates from different outbreaks/incidents. PMID:28166293

Genome-wide SNP discovery and population structure analysis in pepper (Capsicum annuum) using genotyping by sequencing.

PubMed

Taranto, F; D'Agostino, N; Greco, B; Cardi, T; Tripodi, P

2016-11-21

Knowledge on population structure and genetic diversity in vegetable crops is essential for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Herein we used the GBS approach for the genome-wide identification of SNPs in a collection of Capsicum spp. accessions and for the assessment of the level of genetic diversity in a subset of 222 cultivated pepper (Capsicum annum) genotypes. GBS analysis generated a total of 7,568,894 master tags, of which 43.4% uniquely aligned to the reference genome CM334. A total of 108,591 SNP markers were identified, of which 105,184 were in C. annuum accessions. In order to explore the genetic diversity of C. annuum and to select a minimal core set representing most of the total genetic variation with minimum redundancy, a subset of 222 C. annuum accessions were analysed using 32,950 high quality SNPs. Based on Bayesian and Hierarchical clustering it was possible to divide the collection into three clusters. Cluster I had the majority of varieties and landraces mainly from Southern and Northern Italy, and from Eastern Europe, whereas clusters II and III comprised accessions of different geographical origins. Considering the genome-wide genetic variation among the accessions included in cluster I, a second round of Bayesian (K = 3) and Hierarchical (K = 2) clustering was performed. These analysis showed that genotypes were grouped not only based on geographical origin, but also on fruit-related features. GBS data has proven useful to assess the genetic diversity in a collection of C. annuum accessions. The high number of SNP markers, uniformly distributed on the 12 chromosomes, allowed the accessions to be distinguished according to geographical origin and fruit-related features. SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs.

Detecting the influence of ornamental Berberis thunbergii var. atropurpurea in invasive populations of Berberis thunbergii (Berberidaceae) using AFLP1.

PubMed

Lubell, Jessica D; Brand, Mark H; Lehrer, Jonathan M; Holsinger, Kent E

2008-06-01

Japanese barberry (Berberis thunbergii DC.) is a widespread invasive plant that remains an important landscape shrub represented by ornamental, purple-leaved forms of the botanical variety atropurpurea. These forms differ greatly in appearance from feral plants, bringing into question whether they contribute to invasive populations or whether the invasions represent self-sustaining populations derived from the initial introduction of the species in the late 19th century. In this study we used amplified fragment length polymorphism (AFLP) markers to determine whether genetic contributions from B. t. var. atropurpurea are found within naturalized Japanese barberry populations in southern New England. Bayesian clustering of AFLP genotypes and principal coordinate analysis distinguished B. t. var. atropurpurea genotypes from 85 plants representing five invasive populations. While a single feral plant resembled B. t. var. atropurpurea phenotypically and fell within the same genetic cluster, all other naturalized plants sampled were genetically distinct from the purple-leaved genotypes. Seven plants from two different sites possessed morphology consistent with Berberis vulgaris (common barberry) or B. ×ottawensis (B. thunbergii × B. vulgaris). Genetic analysis placed these plants in two clusters separate from B. thunbergii. Although the Bayesian analysis indicated some introgression of B. t. var. atropurpurea and B. vulgaris, these genotypes have had limited influence on extant feral populations of B. thunbergii.

The contribution of cluster and discriminant analysis to the classification of complex aquifer systems.

PubMed

Panagopoulos, G P; Angelopoulou, D; Tzirtzilakis, E E; Giannoulopoulos, P

2016-10-01

This paper presents an innovated method for the discrimination of groundwater samples in common groups representing the hydrogeological units from where they have been pumped. This method proved very efficient even in areas with complex hydrogeological regimes. The proposed method requires chemical analyses of water samples only for major ions, meaning that it is applicable to most of cases worldwide. Another benefit of the method is that it gives a further insight of the aquifer hydrogeochemistry as it provides the ions that are responsible for the discrimination of the group. The procedure begins with cluster analysis of the dataset in order to classify the samples in the corresponding hydrogeological unit. The feasibility of the method is proven from the fact that the samples of volcanic origin were separated into two different clusters, namely the lava units and the pyroclastic-ignimbritic aquifer. The second step is the discriminant analysis of the data which provides the functions that distinguish the groups from each other and the most significant variables that define the hydrochemical composition of the aquifer. The whole procedure was highly successful as the 94.7 % of the samples were classified to the correct aquifer system. Finally, the resulted functions can be safely used to categorize samples of either unknown or doubtful origin improving thus the quality and the size of existing hydrochemical databases.

Physical model of protein cluster positioning in growing bacteria

NASA Astrophysics Data System (ADS)

Wasnik, Vaibhav; Wang, Hui; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2017-10-01

Chemotaxic receptors in bacteria form clusters at cell poles and also laterally, and this clustering plays an important role in signal transduction. These clusters were found to be periodically arranged on the surface of the bacterium Escherichia coli, independent of any known positioning mechanism. In this work we extend a model based on diffusion and aggregation to more realistic geometries and present a means based on ‘bursty’ protein production to distinguish spontaneous positioning from an independently existing positioning mechanism. We also consider the case of isotropic cellular growth and characterize the degree of order arising spontaneously. Our model could also be relevant for other examples of periodically positioned protein clusters in bacteria.

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

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

Melchior, P.; Suchyta, E.; Huff, E.

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modeling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modeling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. In addition, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1 degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.

PubMed

Kopelman, Naama M; Mayzel, Jonathan; Jakobsson, Mattias; Rosenberg, Noah A; Mayrose, Itay

2015-09-01

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology. © 2015 John Wiley & Sons Ltd.

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

DOE PAGES

Melchior, P.; Suchyta, E.; Huff, E.; ...

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modelling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modelling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. Additionally, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

16S ribosomal RNA sequence analysis for determination of phylogenetic relationship among methylotrophs.

PubMed

Tsuji, K; Tsien, H C; Hanson, R S; DePalma, S R; Scholtz, R; LaRoche, S

1990-01-01

16S ribosomal RNAs (rRNA) of 12 methylotrophic bacteria have been almost completely sequenced to establish their phylogenetic relationships. Methylotrophs that are physiologically related are phylogenetically diverse and are scattered among the purple eubacteria (class Proteobacteria). Group I methylotrophs can be classified in the beta- and the gamma-subdivisions and group II methylotrophs in the alpha-subdivision of the purple eubacteria, respectively. Pink-pigmented facultative and non-pigmented obligate group II methylotrophs form two distinctly separate branches within the alpha-subdivision. The secondary structures of the 16S rRNA sequences of 'Methylocystis parvus' strain OBBP, 'Methylosinus trichosporium' strain OB3b, 'Methylosporovibrio methanica' strain 81Z and Hyphomicrobium sp. strain DM2 are similar, and these non-pigmented obligate group II methylotrophs form one tight cluster in the alpha-subdivision. The pink-pigmented facultative methylotrophs, Methylobacterium extorquens strain AM1, Methylobacterium sp. strain DM4 and Methylobacterium organophilum strain XX form another cluster within the alpha-subdivision. Although similar in phenotypic characteristics, Methylobacterium organophilum strain XX and Methylobacterium extorquens strain AM1 are clearly distinguishable by their 16S rRNA sequences. The group I methylotrophs, Methylophilus methylotrophus strain AS1 and methylotrophic species DM11, which do not utilize methane, are similar in 16S rRNA sequence to bacteria in the beta-subdivision. The methane-utilizing, obligate group I methanotrophs, Methylococcus capsulatus strain BATH and Methylomonas methanica, are placed in the gamma-subdivision. The results demonstrate that it is possible to distinguish and classify the methylotrophic bacteria using 16S rRNA sequence analysis.

Data Mining of Satellite-Based Measurements to Distinguish Natural From Man-Made Oil Slicks at the Sea Surface in Campeche Bay (Mexico)

NASA Astrophysics Data System (ADS)

Carvalho, G. D. A.; Minnett, P. J.; de Miranda, F. P.; Landau, L.; Paes, E.

2016-02-01

Campeche Bay, located in the Mexican portion of the Gulf of Mexico, has a well-established activity engaged with numerous oil rigs exploring and producing natural gas and oil. The associated risk of oil slicks in this region - that include oil spills (i.e. oil floating at the sea surface solely attributed to man-made activities) and oil seeps (i.e. surface footprint of the oil that naturally comes out of the seafloor reaching the surface of the ocean) - leads Pemex to be in a continuous state of alert for reducing possible negative influence on marine and coastal ecosystems. Focusing on a monitoring strategy, a multi-year dataset (2008-2012) of synthetic aperture radar (SAR) measurements from the RADARSAT-2 satellite is used to investigate the spatio-temporal distribution of the oil slicks observed at the surface of the ocean in the Campeche Bay region. The present study is an exploratory data analysis that seeks to discriminate between these two possible oil slick types: oil seeps and oil spills. Multivariate data analysis techniques (e.g. Principal Components Analysis, Clustering Analysis, Discriminant Function, etc.) are explored to design a data-learning classification algorithm to distinguish natural from man-made oil slicks. This analysis promotes a novel idea bridging geochemistry and remote sensing research to express geophysical differences between seeped and spilled oil. Here, SAR backscatter coefficients - i.e. sigma-naught (σo), beta-naught (βo), and gamma-naught (γo) - are combined with attributes referring to the geometry, shape, and dimension that describe the oil slicks. Results indicate that the synergy of combining these various characteristics is capable of distinguishing oil seeps from oil spills observed on the sea surface to a useful accuracy.

Dark field microscopic analysis of discrete Au nanostructures: Understanding the correlation of scattering with stoichiometry

NASA Astrophysics Data System (ADS)

Wang, Guoqing; Bu, Tong; Zako, Tamotsu; Watanabe-Tamaki, Ryoko; Tanaka, Takuo; Maeda, Mizuo

2017-09-01

Due to the potential of gold nanoparticle (AuNP)-based trace analysis, the discrimination of small AuNP clusters with different assembling stoichiometry is a subject of fundamental and technological importance. Here we prepare oligomerized AuNPs with controlled stoichiometry through DNA-directed assembly, and demonstrate that AuNP monomers, dimers and trimers can be clearly distinguished using dark field microscopy (DFM). The scattering intensity for of AuNP structures with stoichiometry ranging from 1 to 3 agrees well with our theoretical calculations. This study demonstrates the potential of utilizing the DFM approach in ultra-sensitive detection as well as the use of DNA-directed assembly for plasmonic nano-architectures.

Study on elemental fingerprint of traditional marine Chinese medicine oysters from Jiaozhou Bay, China

NASA Astrophysics Data System (ADS)

Zheng, Yongjun; Zheng, Kang; Li, Yantuan

2012-09-01

In order to investigate the relationship between the trace elements and the characteristics of the oysters, we analyzed the trace elements present in the germplasm of oysters from different producing areas in the Jiaozhou Bay. The element fingerprints were established to reflect the elemental characteristics of the oysters. Concentration patterns of the elements were deciphered by principle component analysis (PCA) and hierarchical cluster analysis (HCA). The six regions were discriminated with accuracy using HCA and PCA based on the concentration of 16 trace elements. The elements were viewed as characteristic elements of the oysters and the fingerprints of these elements could be used to distinguish the quality of the oysters.

Assessing the distinguishable cluster approximation based on the triple bond-breaking in the nitrogen molecule

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

Rishi, Varun; Perera, Ajith; Bartlett, Rodney J., E-mail: bartlett@qtp.ufl.edu

2016-03-28

Obtaining the correct potential energy curves for the dissociation of multiple bonds is a challenging problem for ab initio methods which are affected by the choice of a spin-restricted reference function. Coupled cluster (CC) methods such as CCSD (coupled cluster singles and doubles model) and CCSD(T) (CCSD + perturbative triples) correctly predict the geometry and properties at equilibrium but the process of bond dissociation, particularly when more than one bond is simultaneously broken, is much more complicated. New modifications of CC theory suggest that the deleterious role of the reference function can be diminished, provided a particular subset of termsmore » is retained in the CC equations. The Distinguishable Cluster (DC) approach of Kats and Manby [J. Chem. Phys. 139, 021102 (2013)], seemingly overcomes the deficiencies for some bond-dissociation problems and might be of use in quasi-degenerate situations in general. DC along with other approximate coupled cluster methods such as ACCD (approximate coupled cluster doubles), ACP-D45, ACP-D14, 2CC, and pCCSD(α, β) (all defined in text) falls under a category of methods that are basically obtained by the deletion of some quadratic terms in the double excitation amplitude equation for CCD/CCSD (coupled cluster doubles model/coupled cluster singles and doubles model). Here these approximate methods, particularly those based on the DC approach, are studied in detail for the nitrogen molecule bond-breaking. The N{sub 2} problem is further addressed with conventional single reference methods but based on spatial symmetry-broken restricted Hartree–Fock (HF) solutions to assess the use of these references for correlated calculations in the situation where CC methods using fully symmetry adapted SCF solutions fail. The distinguishable cluster method is generalized: 1) to different orbitals for different spins (unrestricted HF based DCD and DCSD), 2) by adding triples correction perturbatively (DCSD(T)) and iteratively (DCSDT-n), and 3) via an excited state approximation through the equation of motion (EOM) approach (EOM-DCD, EOM-DCSD). The EOM-CC method is used to identify lower-energy CC solutions to overcome singularities in the CC potential energy curves. It is also shown that UHF based CC and DC methods behave very similarly in bond-breaking of N{sub 2}, and that using spatially broken but spin preserving SCF references makes the CCSD solutions better than those for DCSD.« less

Mindfulness-Based Stress Reduction in Post-treatment Breast Cancer Patients: Immediate and Sustained Effects Across Multiple Symptom Clusters.

PubMed

Reich, Richard R; Lengacher, Cecile A; Alinat, Carissa B; Kip, Kevin E; Paterson, Carly; Ramesar, Sophia; Han, Heather S; Ismail-Khan, Roohi; Johnson-Mallard, Versie; Moscoso, Manolete; Budhrani-Shani, Pinky; Shivers, Steve; Cox, Charles E; Goodman, Matthew; Park, Jong

2017-01-01

Breast cancer survivors (BCS) face adverse physical and psychological symptoms, often co-occurring. Biologic and psychological factors may link symptoms within clusters, distinguishable by prevalence and/or severity. Few studies have examined the effects of behavioral interventions or treatment of symptom clusters. The aim of this study was to identify symptom clusters among post-treatment BCS and determine symptom cluster improvement following the Mindfulness-Based Stress Reduction for Breast Cancer (MBSR(BC)) program. Three hundred twenty-two Stage 0-III post-treatment BCS were randomly assigned to either a six-week MBSR(BC) program or usual care. Psychological (depression, anxiety, stress, and fear of recurrence), physical (fatigue, pain, sleep, and drowsiness), and cognitive symptoms and quality of life were assessed at baseline, six, and 12 weeks, along with demographic and clinical history data at baseline. A three-step analytic process included the error-accounting models of factor analysis and structural equation modeling. Four symptom clusters emerged at baseline: pain, psychological, fatigue, and cognitive. From baseline to six weeks, the model demonstrated evidence of MBSR(BC) effectiveness in both the psychological (anxiety, depression, perceived stress and QOL, emotional well-being) (P = 0.007) and fatigue (fatigue, sleep, and drowsiness) (P < 0.001) clusters. Results between six and 12 weeks showed sustained effects, but further improvement was not observed. Our results provide clinical effectiveness evidence that MBSR(BC) works to improve symptom clusters, particularly for psychological and fatigue symptom clusters, with the greatest improvement occurring during the six-week program with sustained effects for several weeks after MBSR(BC) training. Name and URL of Registry: ClinicalTrials.gov. Registration number: NCT01177124. Copyright © 2016. Published by Elsevier Inc.

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

PubMed

Costa, Marta; Manton, James D; Ostrovsky, Aaron D; Prohaska, Steffen; Jefferis, Gregory S X E

2016-07-20

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT. Copyright © 2016 MRC Laboratory of Molecular Biology. Published by Elsevier Inc. All rights reserved.

Carbon and nitrogen abundances in red giant stars in the globular cluster 47 Tucanae

NASA Technical Reports Server (NTRS)

Dickens, R. J.; Bell, R. A.; Gustafsson, B.

1979-01-01

The effects of changes in temperature, gravity, overall metal abundance, and carbon and nitrogen abundances have been investigated for model stellar spectra and colors representing globular-cluster giants of moderate metal deficiency. The results are presented in the form of spectral atlases and theoretical color-color diagrams. Using these results, approximate abundances of carbon and nitrogen have been derived for some red giant stars in 47 Tuc, from intermediate- and low-dispersion spectra and from intermediate- and narrow-band photometry. In all the normal giants studied, nitrogen is overabundant by up to about a factor of 5 (the precise value depends on the adopted carbon abundance), with different enhancements for different giants. The observational material is not sufficient to distinguish between a normal carbon abundance and a slight carbon depletion for the giant-branch stars, but carbon appears to be somewhat depleted in stars on the asymptotic giant branch. A most probable value of M/H = -0.8 for the overall cluster metal abundance is suggested from analysis of Stromgren photometry of red horizontal-branch stars.

Homogeneity tests of clustered diagnostic markers with applications to the BioCycle Study

PubMed Central

Tang, Liansheng Larry; Liu, Aiyi; Schisterman, Enrique F.; Zhou, Xiao-Hua; Liu, Catherine Chun-ling

2014-01-01

Diagnostic trials often require the use of a homogeneity test among several markers. Such a test may be necessary to determine the power both during the design phase and in the initial analysis stage. However, no formal method is available for the power and sample size calculation when the number of markers is greater than two and marker measurements are clustered in subjects. This article presents two procedures for testing the accuracy among clustered diagnostic markers. The first procedure is a test of homogeneity among continuous markers based on a global null hypothesis of the same accuracy. The result under the alternative provides the explicit distribution for the power and sample size calculation. The second procedure is a simultaneous pairwise comparison test based on weighted areas under the receiver operating characteristic curves. This test is particularly useful if a global difference among markers is found by the homogeneity test. We apply our procedures to the BioCycle Study designed to assess and compare the accuracy of hormone and oxidative stress markers in distinguishing women with ovulatory menstrual cycles from those without. PMID:22733707

Environmental clustering of lakes to evaluate performance of a macrophyte index of biotic integrity

USGS Publications Warehouse

Vondracek, Bruce C.; Vondracek, Bruce; Hatch, Lorin K.

2013-01-01

Proper classification of sites is critical for the use of biological indices that can distinguish between natural and human-induced variation in biological response. The macrophyte-based index of biotic integrity was developed to assess the condition of Minnesota lakes in relation to anthropogenic stressors, but macrophyte community composition varies naturally across the state. The goal of the study was to identify environmental characteristics that naturally influence macrophyte index response and establish a preliminary lake classification scheme for biological assessment (bioassessment). Using a comprehensive set of environmental variables, we identified similar groups of lakes by clustering using flexible beta classification. Variance partitioning analysis of IBI response indicated that evaluating similar lake clusters could improve the ability of the macrophyte index to identify community change to anthropogenic stressors, although lake groups did not fully account for the natural variation in macrophyte composition. Diagnostic capabilities of the index could be improved when evaluating lakes with similar environmental characteristics, suggesting the index has potential for accurate bioassessment provided comparable groups of lakes are evaluated.

Lipopolysaccharide biosynthesis genes discriminate between Rubus- and Spiraeoideae-infective genotypes of Erwinia amylovora.

PubMed

Rezzonico, Fabio; Braun-Kiewnick, Andrea; Mann, Rachel A; Rodoni, Brendan; Goesmann, Alexander; Duffy, Brion; Smits, Theo H M

2012-10-01

Comparative genomic analysis revealed differences in the lipopolysaccharide (LPS) biosynthesis gene cluster between the Rubus-infecting strain ATCC BAA-2158 and the Spiraeoideae-infecting strain CFBP 1430 of Erwinia amylovora. These differences corroborate rpoB-based phylogenetic clustering of E. amylovora into four different groups and enable the discrimination of Spiraeoideae- and Rubus-infecting strains. The structure of the differences between the two groups supports the hypothesis that adaptation to Rubus spp. took place after species separation of E. amylovora and E. pyrifoliae that contrasts with a recently proposed scenario, based on CRISPR data, in which the shift to domesticated apple would have caused an evolutionary bottleneck in the Spiraeoideae-infecting strains of E. amylovora which would be a much earlier event. In the core region of the LPS biosynthetic gene cluster, Spiraeoideae-infecting strains encode three glycosyltransferases and an LPS ligase (Spiraeoideae-type waaL), whereas Rubus-infecting strains encode two glycosyltransferases and a different LPS ligase (Rubus-type waaL). These coding domains share little to no homology at the amino acid level between Rubus- and Spiraeoideae-infecting strains, and this genotypic difference was confirmed by polymerase chain reaction analysis of the associated DNA region in 31 Rubus- and Spiraeoideae-infecting strains. The LPS biosynthesis gene cluster may thus be used as a molecular marker to distinguish between Rubus- and Spiraeoideae-infecting strains of E. amylovora using primers designed in this study. © 2012 THE AUTHORS. MOLECULAR PLANT PATHOLOGY © 2012 BSPP AND BLACKWELL PUBLISHING LTD.

Do Staphylococcus epidermidis Genetic Clusters Predict Isolation Sources?

PubMed Central

Tolo, Isaiah; Thomas, Jonathan C.; Fischer, Rebecca S. B.; Brown, Eric L.; Gray, Barry M.

2016-01-01

Staphylococcus epidermidis is a ubiquitous colonizer of human skin and a common cause of medical device-associated infections. The extent to which the population genetic structure of S. epidermidis distinguishes commensal from pathogenic isolates is unclear. Previously, Bayesian clustering of 437 multilocus sequence types (STs) in the international database revealed a population structure of six genetic clusters (GCs) that may reflect the species' ecology. Here, we first verified the presence of six GCs, including two (GC3 and GC5) with significant admixture, in an updated database of 578 STs. Next, a single nucleotide polymorphism (SNP) assay was developed that accurately assigned 545 (94%) of 578 STs to GCs. Finally, the hypothesis that GCs could distinguish isolation sources was tested by SNP typing and GC assignment of 154 isolates from hospital patients with bacteremia and those with blood culture contaminants and from nonhospital carriage. GC5 was isolated almost exclusively from hospital sources. GC1 and GC6 were isolated from all sources but were overrepresented in isolates from nonhospital and infection sources, respectively. GC2, GC3, and GC4 were relatively rare in this collection. No association was detected between fdh-positive isolates (GC2 and GC4) and nonhospital sources. Using a machine learning algorithm, GCs predicted hospital and nonhospital sources with 80% accuracy and predicted infection and contaminant sources with 45% accuracy, which was comparable to the results seen with a combination of five genetic markers (icaA, IS256, sesD [bhp], mecA, and arginine catabolic mobile element [ACME]). Thus, analysis of population structure with subgenomic data shows the distinction of hospital and nonhospital sources and the near-inseparability of sources within a hospital. PMID:27076664

MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability.

PubMed

Olivon, Florent; Grelier, Gwendal; Roussi, Fanny; Litaudon, Marc; Touboul, David

2017-08-01

Molecular networking is becoming more and more popular into the metabolomic community to organize tandem mass spectrometry (MS 2 ) data. Even though this approach allows the treatment and comparison of large data sets, several drawbacks related to the MS-Cluster tool routinely used on the Global Natural Product Social Molecular Networking platform (GNPS) limit its potential. MS-Cluster cannot distinguish between chromatography well-resolved isomers as retention times are not taken into account. Annotation with predicted chemical formulas is also not implemented and semiquantification is only based on the number of MS 2 scans. We propose to introduce a data-preprocessing workflow including the preliminary data treatment by MZmine 2 followed by a homemade Python script freely available to the community that clears the major previously mentioned GNPS drawbacks. The efficiency of this workflow is exemplified with the analysis of six fractions of increasing polarities obtained from a sequential supercritical CO 2 extraction of Stillingia lineata leaves.

Localizing the Position of an Ultraluminous X-ray Flare in an Extragalactic Globular Cluster

NASA Astrophysics Data System (ADS)

Irwin, Jimmy

2017-09-01

X-ray timing analysis has revealed two extragalactic sources that flare well above L_Edd for a stellar-mass BH by factors of >100 on time scales of less than a minute, joining only SGRs/AXPs in this category. One of these flares is coincident with the massive globular cluster/ultracompact dwarf galaxy of the elliptical galaxy NGC5128 known as HGHH-C21, which has a resolvable half-light radius of 0.4". Previous observations of the flare were far off-axis where the Chandra PSF was quite large, precluding an accurate position determination of the flare source within HHGH-C21. We propose an 80 ksec ACIS-S on-axis observation of the flare to determine the flare's position within HHGH-C21 to <0.2" uncertainty to distinguish between intermediate-mass BH and exotic accretion mechanism scenarios.

AGB subpopulations in the nearby globular cluster NGC 6397

NASA Astrophysics Data System (ADS)

MacLean, B. T.; Campbell, S. W.; De Silva, G. M.; Lattanzio, J.; D'Orazi, V.; Cottrell, P. L.; Momany, Y.; Casagrande, L.

2018-03-01

It has been well established that Galactic Globular clusters (GCs) harbour more than one stellar population, distinguishable by the anticorrelations of light-element abundances (C-N, Na-O, and Mg-Al). These studies have been extended recently to the asymptotic giant branch (AGB). Here, we investigate the AGB of NGC 6397 for the first time. We have performed an abundance analysis of high-resolution spectra of 47 red giant branch (RGB) and eight AGB stars, deriving Fe, Na, O, Mg, and Al abundances. We find that NGC 6397 shows no evidence of a deficit in Na-rich AGB stars, as reported for some other GCs - the subpopulation ratios of the AGB and RGB in NGC 6397 are identical, within uncertainties. This agrees with expectations from stellar theory. This GC acts as a control for our earlier work on the AGB of M4 (with contrasting results), since the same tools and methods were used.

Observing interactions between DNA bases using ion dip spectroscopy.

NASA Astrophysics Data System (ADS)

Vries Mattanjah, De

2002-03-01

We investigate biomolecular building blocks and their clusters with each other and with water on a single molecular level. The motivation is the need to distinguish between intrinsic molecular properties and those that result from the biological environment. This is achieved by a combination of laser desorption and jet cooling, applied to aromatic amino acids, small peptides containing those, purine bases and nucleosides. This approach is coupled with a number of gas phase laser spectroscopic techniques. We will present results for DNA bases guanine, adenine, cytosine, and their derivatives, for which we obtained tautomer selected vibronic spectra. Capitalizing on these results we use these bases as chromophores to study interactions in single base pairs, obtained by formation of clusters of laser desorbed bases in a supersonic beam. For analysis we employ both UV/UV and IR/UV ion-dip spectroscopy, the results of which we compare with ab initio calculations.

Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis.

PubMed

Ocké, Marga C

2013-05-01

This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.

Non-specific filtering of beta-distributed data.

PubMed

Wang, Xinhui; Laird, Peter W; Hinoue, Toshinori; Groshen, Susan; Siegmund, Kimberly D

2014-06-19

Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias. We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets. We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered.

Principal components derived from CSF inflammatory profiles predict outcome in survivors after severe traumatic brain injury.

PubMed

Kumar, Raj G; Rubin, Jonathan E; Berger, Rachel P; Kochanek, Patrick M; Wagner, Amy K

2016-03-01

Studies have characterized absolute levels of multiple inflammatory markers as significant risk factors for poor outcomes after traumatic brain injury (TBI). However, inflammatory marker concentrations are highly inter-related, and production of one may result in the production or regulation of another. Therefore, a more comprehensive characterization of the inflammatory response post-TBI should consider relative levels of markers in the inflammatory pathway. We used principal component analysis (PCA) as a dimension-reduction technique to characterize the sets of markers that contribute independently to variability in cerebrospinal (CSF) inflammatory profiles after TBI. Using PCA results, we defined groups (or clusters) of individuals (n=111) with similar patterns of acute CSF inflammation that were then evaluated in the context of outcome and other relevant CSF and serum biomarkers collected days 0-3 and 4-5 post-injury. We identified four significant principal components (PC1-PC4) for CSF inflammation from days 0-3, and PC1 accounted for the greatest (31%) percentage of variance. PC1 was characterized by relatively higher CSF sICAM-1, sFAS, IL-10, IL-6, sVCAM-1, IL-5, and IL-8 levels. Cluster analysis then defined two distinct clusters, such that individuals in cluster 1 had highly positive PC1 scores and relatively higher levels of CSF cortisol, progesterone, estradiol, testosterone, brain derived neurotrophic factor (BDNF), and S100b; this group also had higher serum cortisol and lower serum BDNF. Multinomial logistic regression analyses showed that individuals in cluster 1 had a 10.9 times increased likelihood of GOS scores of 2/3 vs. 4/5 at 6 months compared to cluster 2, after controlling for covariates. Cluster group did not discriminate between mortality compared to GOS scores of 4/5 after controlling for age and other covariates. Cluster groupings also did not discriminate mortality or 12 month outcomes in multivariate models. PCA and cluster analysis establish that a subset of CSF inflammatory markers measured in days 0-3 post-TBI may distinguish individuals with poor 6-month outcome, and future studies should prospectively validate these findings. PCA of inflammatory mediators after TBI could aid in prognostication and in identifying patient subgroups for therapeutic interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

PubMed Central

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; MacEachren, Alan M

2008-01-01

Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. Conclusion The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. Method We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit. PMID:18992163

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

PubMed

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

2008-11-07

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

YOUNG STELLAR CLUSTERS CONTAINING MASSIVE YOUNG STELLAR OBJECTS IN THE VVV SURVEY

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

Borissova, J.; Alegría, S. Ramírez; Kurtev, R.

The purpose of this research is to study the connections of the global properties of eight young stellar clusters projected in the Vista Variables in the Via Lactea (VVV) ESO Large Public Survey disk area and their young stellar object (YSO) populations. The analysis is based on the combination of spectroscopic parallax-based reddening and distance determinations with main-sequence and pre-main-sequence ishochrone fitting to determine the basic parameters (reddening, age, distance) of the sample clusters. The lower mass limit estimations show that all clusters are low or intermediate mass (between 110 and 1800  M {sub ⊙}), the slope Γ of themore » obtained present-day mass functions of the clusters is close to the Kroupa initial mass function. The YSOs in the cluster’s surrounding fields are classified using low resolution spectra, spectral energy distribution fits with theoretical predictions, and variability, taking advantage of multi-epoch VVV observations. All spectroscopically confirmed YSOs (except one) are found to be massive (more than 8 M {sub ⊙}). Using VVV and GLIMPSE color–color cuts we have selected a large number of new YSO candidates, which are checked for variability and 57% are found to show at least low-amplitude variations. In few cases it was possible to distinguish between YSO and AGB classifications on the basis of light curves.« less

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

PubMed

Ng, Edmond S-W; Diaz-Ordaz, Karla; Grieve, Richard; Nixon, Richard M; Thompson, Simon G; Carpenter, James R

2016-10-01

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data. © The Author(s) 2013.

Variation in essential oil composition of Teucrium hircanicum L. from Iran-A rich source of (E)-α-bergamotene.

PubMed

Rahimi, Mohammad Ali; Nazeri, Vahideh; Andi, Seyed Ali; Sefidkon, Fatemeh

2018-05-21

In present work, the chemical composition of the essential oils obtained from dried flowering aerial parts of Teucrium hircanicum L. (Labiatae) originated from ten wild populations in Iran was analyzed by a GC-FID and GC/MS system. The oil yields varied from 0.04% to 0.1%. A total of thirty-two compounds representing 67.6-97.7% of the oil were identified. The essential oil was found to be rich in sesquiterpene hydrocarpons (E)-α-bergamotene (17.5-86.9%) and (E)-β-farnesene (0.5-21.4%). Of the total identified compounds, sesquiterpene hydrocarpons (36.1-89.7%) were included the greatest essential oil fraction in all the populations, followed by oxygenated monoterpenes (2.2-21.6%), oxygenated sesquiterpenes (0.0-14.4%) and monoterepene hydrocarbons (0.0-9.5%). Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to distinguish any geographical variations, indicating that the clustering of populations is related to their geographic origin. According to the GC/MS analysis, two chemotypes consisting of (E)-α-bergamotene and (E)-α-bergamotene-(E)-β-farnesene were identified in the populations.

Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

NASA Astrophysics Data System (ADS)

Farics, Éva; Farics, Dávid; Kovács, József; Haas, János

2017-10-01

The main aim of this paper is to determine the depositional environments of an Upper-Eocene coarse-grained clastic succession in the Buda Hills, Hungary. First of all, we measured some commonly used parameters of samples (size, amount, roundness and sphericity) in a much more objective overall and faster way than with traditional measurement approaches, using the newly developed Rock Analyst application. For the multivariate data obtained, we applied Combined Cluster and Discriminant Analysis (CCDA) in order to determine homogeneous groups of the sampling locations based on the quantitative composition of the conglomerate as well as the shape parameters (roundness and sphericity). The result is the spatial pattern of these groups, which assists with the interpretation of the depositional processes. According to our concept, those sampling sites which belong to the same homogeneous groups were likely formed under similar geological circumstances and by similar geological processes. In the Buda Hills, we were able to distinguish various sedimentological environments within the area based on the results: fan, intermittent stream or marine.

Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

PubMed

de Luca, Aurélie; Horvath, Dragos; Marcou, Gilles; Solov'ev, Vitaly; Varnek, Alexandre

2012-09-24

This work addresses the problem of similarity search and classification of chemical reactions using Neighborhood Behavior (NB) and Condensed Graphs of Reaction (CGR) approaches. The CGR formalism represents chemical reactions as a classical molecular graph with dynamic bonds, enabling descriptor calculations on this graph. Different types of the ISIDA fragment descriptors generated for CGRs in combination with two metrics--Tanimoto and Euclidean--were considered as chemical spaces, to serve for reaction dissimilarity scoring. The NB method has been used to select an optimal combination of descriptors which distinguish different types of chemical reactions in a database containing 8544 reactions of 9 classes. Relevance of NB analysis has been validated in generic (multiclass) similarity search and in clustering with Self-Organizing Maps (SOM). NB-compliant sets of descriptors were shown to display enhanced mapping propensities, allowing the construction of better Self-Organizing Maps and similarity searches (NB and classical similarity search criteria--AUC ROC--correlate at a level of 0.7). The analysis of the SOM clusters proved chemically meaningful CGR substructures representing specific reaction signatures.

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

Healthcare managers' leadership profiles in relation to perceptions of work stressors and stress.

PubMed

Lornudd, Caroline; Bergman, David; Sandahl, Christer; von Thiele Schwarz, Ulrica

2016-05-03

Purpose The purpose of this study is to investigate the relationship between leadership profiles and differences in managers' own levels of work stress symptoms and perceptions of work stressors causing stress. Design/methodology/approach Cross-sectional data were used. Healthcare managers ( n = 188) rated three dimensions of their leadership behavior and levels of work stressors and stress. Hierarchical cluster analysis was performed to identify leadership profiles based on leadership behaviors. Differences in stress-related outcomes between profiles were assessed using one-way analysis of variance. Findings Four distinct clusters of leadership profiles were found. They discriminated in perception of work stressors and stress: the profile distinguished by the lowest mean in all behavior dimensions, exhibited a pattern with significantly more negative ratings compared to the other profiles. Practical implications This paper proposes that leadership profile is an individual factor involved in the stress process, including work stressors and stress, which may inform targeted health promoting interventions for healthcare managers. Originality/value This is the first study to investigate the relationship between leadership profiles and work stressors and stress in healthcare managers.

Turbulence and vorticity in Galaxy clusters generated by structure formation

NASA Astrophysics Data System (ADS)

Vazza, F.; Jones, T. W.; Brüggen, M.; Brunetti, G.; Gheller, C.; Porter, D.; Ryu, D.

2017-01-01

Turbulence is a key ingredient for the evolution of the intracluster medium, whose properties can be predicted with high-resolution numerical simulations. We present initial results on the generation of solenoidal and compressive turbulence in the intracluster medium during the formation of a small-size cluster using highly resolved, non-radiative cosmological simulations, with a refined monitoring in time. In this first of a series of papers, we closely look at one simulated cluster whose formation was distinguished by a merger around z ˜ 0.3. We separate laminar gas motions, turbulence and shocks with dedicated filtering strategies and distinguish the solenoidal and compressive components of the gas flows using Hodge-Helmholtz decomposition. Solenoidal turbulence dominates the dissipation of turbulent motions (˜95 per cent) in the central cluster volume at all epochs. The dissipation via compressive modes is found to be more important (˜30 per cent of the total) only at large radii (≥0.5rvir) and close to merger events. We show that enstrophy (vorticity squared) is good proxy of solenoidal turbulence. All terms ruling the evolution of enstrophy (I.e. baroclinic, compressive, stretching and advective terms) are found to be significant, but in amounts that vary with time and location. Two important trends for the growth of enstrophy in our simulation are identified: first, enstrophy is continuously accreted into the cluster from the outside, and most of that accreted enstrophy is generated near the outer accretion shocks by baroclinic and compressive processes. Secondly, in the cluster interior vortex, stretching is dominant, although the other terms also contribute substantially.

The spatio-temporal mapping of epileptic networks: Combination of EEG–fMRI and EEG source imaging

PubMed Central

Vulliemoz, S.; Thornton, R.; Rodionov, R.; Carmichael, D.W.; Guye, M.; Lhatoo, S.; McEvoy, A.W.; Spinelli, L.; Michel, C.M.; Duncan, J.S.; Lemieux, L.

2009-01-01

Simultaneous EEG–fMRI acquisitions in patients with epilepsy often reveal distributed patterns of Blood Oxygen Level Dependant (BOLD) change correlated with epileptiform discharges. We investigated if electrical source imaging (ESI) performed on the interictal epileptiform discharges (IED) acquired during fMRI acquisition could be used to study the dynamics of the networks identified by the BOLD effect, thereby avoiding the limitations of combining results from separate recordings. Nine selected patients (13 IED types identified) with focal epilepsy underwent EEG–fMRI. Statistical analysis was performed using SPM5 to create BOLD maps. ESI was performed on the IED recorded during fMRI acquisition using a realistic head model (SMAC) and a distributed linear inverse solution (LAURA). ESI could not be performed in one case. In 10/12 remaining studies, ESI at IED onset (ESIo) was anatomically close to one BOLD cluster. Interestingly, ESIo was closest to the positive BOLD cluster with maximal statistical significance in only 4/12 cases and closest to negative BOLD responses in 4/12 cases. Very small BOLD clusters could also have clinical relevance in some cases. ESI at later time frame (ESIp) showed propagation to remote sources co-localised with other BOLD clusters in half of cases. In concordant cases, the distance between maxima of ESI and the closest EEG–fMRI cluster was less than 33 mm, in agreement with previous studies. We conclude that simultaneous ESI and EEG–fMRI analysis may be able to distinguish areas of BOLD response related to initiation of IED from propagation areas. This combination provides new opportunities for investigating epileptic networks. PMID:19408351

Comprehensive Biothreat Cluster Identification by PCR/Electrospray-Ionization Mass Spectrometry

PubMed Central

Sampath, Rangarajan; Mulholland, Niveen; Blyn, Lawrence B.; Massire, Christian; Whitehouse, Chris A.; Waybright, Nicole; Harter, Courtney; Bogan, Joseph; Miranda, Mary Sue; Smith, David; Baldwin, Carson; Wolcott, Mark; Norwood, David; Kreft, Rachael; Frinder, Mark; Lovari, Robert; Yasuda, Irene; Matthews, Heather; Toleno, Donna; Housley, Roberta; Duncan, David; Li, Feng; Warren, Robin; Eshoo, Mark W.; Hall, Thomas A.; Hofstadler, Steven A.; Ecker, David J.

2012-01-01

Technology for comprehensive identification of biothreats in environmental and clinical specimens is needed to protect citizens in the case of a biological attack. This is a challenge because there are dozens of bacterial and viral species that might be used in a biological attack and many have closely related near-neighbor organisms that are harmless. The biothreat agent, along with its near neighbors, can be thought of as a biothreat cluster or a biocluster for short. The ability to comprehensively detect the important biothreat clusters with resolution sufficient to distinguish the near neighbors with an extremely low false positive rate is required. A technological solution to this problem can be achieved by coupling biothreat group-specific PCR with electrospray ionization mass spectrometry (PCR/ESI-MS). The biothreat assay described here detects ten bacterial and four viral biothreat clusters on the NIAID priority pathogen and HHS/USDA select agent lists. Detection of each of the biothreat clusters was validated by analysis of a broad collection of biothreat organisms and near neighbors prepared by spiking biothreat nucleic acids into nucleic acids extracted from filtered environmental air. Analytical experiments were carried out to determine breadth of coverage, limits of detection, linearity, sensitivity, and specificity. Further, the assay breadth was demonstrated by testing a diverse collection of organisms from each biothreat cluster. The biothreat assay as configured was able to detect all the target organism clusters and did not misidentify any of the near-neighbor organisms as threats. Coupling biothreat cluster-specific PCR to electrospray ionization mass spectrometry simultaneously provides the breadth of coverage, discrimination of near neighbors, and an extremely low false positive rate due to the requirement that an amplicon with a precise base composition of a biothreat agent be detected by mass spectrometry. PMID:22768032

Exploring Parental Bonding in BED and Non-BED Obesity Compared with Healthy Controls: Clinical, Personality and Psychopathology Correlates.

PubMed

Amianto, Federico; Ercole, Roberta; Abbate Daga, Giovanni; Fassino, Secondo

2016-05-01

Early inadequate attachment experiences are relevant co-factors in the development of obesity and Binge Eating Disorder (BED), which often concurs with obesity. The relationship of parental bonding with personality and psychopathology may influence treatment strategies for obese subjects, either affected or not with BED. In this study, 443 obese women (BMI ≥ 30 kg/m(2)), including 243 with and 200 without BED, and 158 female controls were assessed with regards to attachment, personality and eating psychopathology measures. Clusters obtained using the scores of the Parental Bonding Instrument (PBI) were compared with each other and with a control subjects' group. Lower scores of parental bonding distinguished obese subjects with respect to healthy controls. The cluster analysis revealed two clusters of parenting among obese subjects. The larger one displayed intermediate care and overprotection between controls and the smaller cluster, with the exception of paternal overprotection which is similar to controls. This larger cluster was characterized by low persistence and levels of psychopathology which are intermediate between healthy controls and the smaller cluster. The smaller cluster displayed lower care and higher overcontrol from both parents. It also displays more extreme personality traits (high novelty seeking and harm avoidance, and lower self-directedness and cooperativeness) and more severe eating and general psychopathology. Different parenting dynamics relate to different personality patterns and eating psychopathology of obese subjects, but not to binge eating conducts. Personality differences between parenting clusters are more extensive than those between BED and non-BED subgroups. The two different typologies of obese subjects based on parenting may be relevant for treatment personalization. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Understanding Genetic Diversity and Population Structure of a Poa pratensis Worldwide Collection through Morphological, Nuclear and Chloroplast Diversity Analysis

PubMed Central

Russi, Luigi; Marconi, Gianpiero; Sharbel, Timothy F.; Veronesi, Fabio; Albertini, Emidio

2015-01-01

Poa pratensis L. is a forage and turf grass species well adapted to a wide range of mesic to moist habitats. Due to its genome complexity little is known regarding evolution, genome composition and intraspecific phylogenetic relationships of this species. In the present study we investigated the morphological and genetic diversity of 33 P. pratensis accessions from 23 different countries using both nuclear and chloroplast molecular markers as well as flow cytometry of somatic tissues. This with the aim of shedding light on the genetic diversity and phylogenetic relationships of the collection that includes both cultivated and wild materials. Morphological characterization showed that the most relevant traits able to distinguish cultivated from wild forms were spring growth habit and leaf colour. The genome size analysis revealed high variability both within and between accessions in both wild and cultivated materials. The sequence analysis of the trnL-F chloroplast region revealed a low polymorphism level that could be the result of the complex mode of reproduction of this species. In addition, a strong reduction of chloroplast SSR variability was detected in cultivated materials, where only two alleles were conserved out of the four present in wild accessions. Contrarily, at nuclear level, high variability exist in the collection where the analysis of 11 SSR loci allowed the detection of a total of 91 different alleles. A Bayesian analysis performed on nuclear SSR data revealed that studied materials belong to two main clusters. While wild materials are equally represented in both clusters, the domesticated forms are mostly belonging to cluster P2 which is characterized by lower genetic diversity compared to the cluster P1. In the Neighbour Joining tree no clear distinction was found between accessions with the exception of those from China and Mongolia that were clearly separated from all the others. PMID:25893249

Analysis of the Seismicity Preceding Large Earthquakes

NASA Astrophysics Data System (ADS)

Stallone, A.; Marzocchi, W.

2016-12-01

The most common earthquake forecasting models assume that the magnitude of the next earthquake is independent from the past. This feature is probably one of the most severe limitations of the capability to forecast large earthquakes.In this work, we investigate empirically on this specific aspect, exploring whether spatial-temporal variations in seismicity encode some information on the magnitude of the future earthquakes. For this purpose, and to verify the universality of the findings, we consider seismic catalogs covering quite different space-time-magnitude windows, such as the Alto Tiberina Near Fault Observatory (TABOO) catalogue, and the California and Japanese seismic catalog. Our method is inspired by the statistical methodology proposed by Zaliapin (2013) to distinguish triggered and background earthquakes, using the nearest-neighbor clustering analysis in a two-dimension plan defined by rescaled time and space. In particular, we generalize the metric based on the nearest-neighbor to a metric based on the k-nearest-neighbors clustering analysis that allows us to consider the overall space-time-magnitude distribution of k-earthquakes (k-foreshocks) which anticipate one target event (the mainshock); then we analyze the statistical properties of the clusters identified in this rescaled space. In essence, the main goal of this study is to verify if different classes of mainshock magnitudes are characterized by distinctive k-foreshocks distribution. The final step is to show how the findings of this work may (or not) improve the skill of existing earthquake forecasting models.

Algorithm of reducing the false positives in IDS based on correlation Analysis

NASA Astrophysics Data System (ADS)

Liu, Jianyi; Li, Sida; Zhang, Ru

2018-03-01

This paper proposes an algorithm of reducing the false positives in IDS based on correlation Analysis. Firstly, the algorithm analyzes the distinguishing characteristics of false positives and real alarms, and preliminary screen the false positives; then use the method of attribute similarity clustering to the alarms and further reduces the amount of alarms; finally, according to the characteristics of multi-step attack, associated it by the causal relationship. The paper also proposed a reverse causation algorithm based on the attack association method proposed by the predecessors, turning alarm information into a complete attack path. Experiments show that the algorithm simplifies the number of alarms, improve the efficiency of alarm processing, and contribute to attack purposes identification and alarm accuracy improvement.

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-legal representation when differentiating the strategic orientations and impact of worker health and safety representatives, with important implications for education, political support and recruitment. © 2015 Wiley Periodicals, Inc.

Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study.

PubMed

Pang, Yuanjie; Peng, Roger D; Jones, Miranda R; Francesconi, Kevin A; Goessler, Walter; Howard, Barbara V; Umans, Jason G; Best, Lyle G; Guallar, Eliseo; Post, Wendy S; Kaufman, Joel D; Vaidya, Dhananjay; Navas-Acien, Ana

2016-05-01

Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000-2002) and 277 American Indian participants in SHS (1998-2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US. Copyright © 2016 Elsevier Inc. All rights reserved.

A modern approach to the authentication and quality assessment of thyme using UV spectroscopy and chemometric analysis.

PubMed

Gad, Haidy A; El-Ahmady, Sherweit H; Abou-Shoer, Mohamed I; Al-Azizi, Mohamed M

2013-01-01

Recently, the fields of chemometrics and multivariate analysis have been widely implemented in the quality control of herbal drugs to produce precise results, which is crucial in the field of medicine. Thyme represents an essential medicinal herb that is constantly adulterated due to its resemblance to many other plants with similar organoleptic properties. To establish a simple model for the quality assessment of Thymus species using UV spectroscopy together with known chemometric techniques. The success of this model may also serve as a technique for the quality control of other herbal drugs. The model was constructed using 30 samples of authenticated Thymus vulgaris and challenged with 20 samples of different botanical origins. The methanolic extracts of all samples were assessed using UV spectroscopy together with chemometric techniques: principal component analysis (PCA), soft independent modeling of class analogy (SIMCA) and hierarchical cluster analysis (HCA). The model was able to discriminate T. vulgaris from other Thymus, Satureja, Origanum, Plectranthus and Eriocephalus species, all traded in the Egyptian market as different types of thyme. The model was also able to classify closely related species in clusters using PCA and HCA. The model was finally used to classify 12 commercial thyme varieties into clusters of species incorporated in the model as thyme or non-thyme. The model constructed is highly recommended as a simple and efficient method for distinguishing T. vulgaris from other related species as well as the classification of marketed herbs as thyme or non-thyme. Copyright © 2013 John Wiley & Sons, Ltd.

Morphology delimits more species than molecular genetic clusters of invasive Pilosella.

PubMed

Moffat, Chandra E; Ensing, David J; Gaskin, John F; De Clerck-Floate, Rosemarie A; Pither, Jason

2015-07-01

• Accurate assessments of biodiversity are paramount for understanding ecosystem processes and adaptation to change. Invasive species often contribute substantially to local biodiversity; correctly identifying and distinguishing invaders is thus necessary to assess their potential impacts. We compared the reliability of morphology and molecular sequences to discriminate six putative species of invasive Pilosella hawkweeds (syn. Hieracium, Asteraceae), known for unreliable identifications and historical introgression. We asked (1) which morphological traits dependably discriminate putative species, (2) if genetic clusters supported morphological species, and (3) if novel hybridizations occur in the invaded range.• We assessed 33 morphometric characters for their discriminatory power using the randomForest classifier and, using AFLPs, evaluated genetic clustering with the program structure and subsequently with an AMOVA. The strength of the association between morphological and genotypic dissimilarity was assessed with a Mantel test.• Morphometric analyses delimited six species while genetic analyses defined only four clusters. Specifically, we found (1) eight morphological traits could reliably distinguish species, (2) structure suggested strong genetic differentiation but for only four putative species clusters, and (3) genetic data suggest both novel hybridizations and multiple introductions have occurred.• (1) Traditional floristic techniques may resolve more species than molecular analyses in taxonomic groups subject to introgression. (2) Even within complexes of closely related species, relatively few but highly discerning morphological characters can reliably discriminate species. (3) By clarifying patterns of morphological and genotypic variation of invasive Pilosella, we lay foundations for further ecological study and mitigation. © 2015 Botanical Society of America, Inc.

Global analysis of saliva as a source of bacterial genes for insights into human population structure and migration studies.

PubMed

Henne, Karsten; Li, Jing; Stoneking, Mark; Kessler, Olga; Schilling, Hildegard; Sonanini, Anne; Conrads, Georg; Horz, Hans-Peter

2014-08-22

The genetic diversity of the human microbiome holds great potential for shedding light on the history of our ancestors. Helicobacter pylori is the most prominent example as its analysis allowed a fine-scale resolution of past migration patterns including some that could not be distinguished using human genetic markers. However studies of H. pylori require stomach biopsies, which severely limits the number of samples that can be analysed. By focussing on the house-keeping gene gdh (coding for the glucose-6-phosphate dehydrogenase), on the virulence gene gtf (coding for the glucosyltransferase) of mitis-streptococci and on the 16S-23S rRNA internal transcribed spacer (ITS) region of the Fusobacterium nucleatum/periodonticum-group we here tested the hypothesis that bacterial genes from human saliva have the potential for distinguishing human populations. Analysis of 10 individuals from each of seven geographic regions, encompassing Africa, Asia and Europe, revealed that the genes gdh and ITS exhibited the highest number of polymorphic sites (59% and 79%, respectively) and most OTUs (defined at 99% identity) were unique to a given country. In contrast, the gene gtf had the lowest number of polymorphic sites (21%), and most OTUs were shared among countries. Most of the variation in the gdh and ITS genes was explained by the high clonal diversity within individuals (around 80%) followed by inter-individual variation of around 20%, leaving the geographic region as providing virtually no source of sequence variation. Conversely, for gtf the variation within individuals accounted for 32%, between individuals for 57% and among geographic regions for 11%. This geographic signature persisted upon extension of the analysis to four additional locations from the American continent. Pearson correlation analysis, pairwise Fst-cluster analysis as well as UniFrac analyses consistently supported a tree structure in which the European countries clustered tightly together and branched with American countries and South Africa, to the exclusion of Asian countries and the Congo. This study shows that saliva harbours protein-coding bacterial genes that are geographically structured, and which could potentially be used for addressing previously unresolved human migration events.

Metagenomic Analyses Reveal That Energy Transfer Gene Abundances Can Predict the Syntrophic Potential of Environmental Microbial Communities

PubMed Central

Oberding, Lisa; Gieg, Lisa M.

2016-01-01

Hydrocarbon compounds can be biodegraded by anaerobic microorganisms to form methane through an energetically interdependent metabolic process known as syntrophy. The microorganisms that perform this process as well as the energy transfer mechanisms involved are difficult to study and thus are still poorly understood, especially on an environmental scale. Here, metagenomic data was analyzed for specific clusters of orthologous groups (COGs) related to key energy transfer genes thus far identified in syntrophic bacteria, and principal component analysis was used in order to determine whether potentially syntrophic environments could be distinguished using these syntroph related COGs as opposed to universally present COGs. We found that COGs related to hydrogenase and formate dehydrogenase genes were able to distinguish known syntrophic consortia and environments with the potential for syntrophy from non-syntrophic environments, indicating that these COGs could be used as a tool to identify syntrophic hydrocarbon biodegrading environments using metagenomic data. PMID:27681901

Anomaly metrics to differentiate threat sources from benign sources in primary vehicle screening.

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

Cohen, Israel Dov; Mengesha, Wondwosen

2011-09-01

Discrimination of benign sources from threat sources at Port of Entries (POE) is of a great importance in efficient screening of cargo and vehicles using Radiation Portal Monitors (RPM). Currently RPM's ability to distinguish these radiological sources is seriously hampered by the energy resolution of the deployed RPMs. As naturally occurring radioactive materials (NORM) are ubiquitous in commerce, false alarms are problematic as they require additional resources in secondary inspection in addition to impacts on commerce. To increase the sensitivity of such detection systems without increasing false alarm rates, alarm metrics need to incorporate the ability to distinguish benign andmore » threat sources. Principal component analysis (PCA) and clustering technique were implemented in the present study. Such techniques were investigated for their potential to lower false alarm rates and/or increase sensitivity to weaker threat sources without loss of specificity. Results of the investigation demonstrated improved sensitivity and specificity in discriminating benign sources from threat sources.« less

Lipid Multilayer Grating Arrays Integrated by Nanointaglio for Vapor Sensing by an Optical Nose

PubMed Central

Lowry, Troy W.; Prommapan, Plengchart; Rainer, Quinn; Van Winkle, David; Lenhert, Steven

2015-01-01

Lipid multilayer gratings are recently invented nanomechanical sensor elements that are capable of transducing molecular binding to fluid lipid multilayers into optical signals in a label free manner due to shape changes in the lipid nanostructures. Here, we show that nanointaglio is suitable for the integration of chemically different lipid multilayer gratings into a sensor array capable of distinguishing vapors by means of an optical nose. Sensor arrays composed of six different lipid formulations are integrated onto a surface and their optical response to three different vapors (water, ethanol and acetone) in air as well as pH under water is monitored as a function of time. Principal component analysis of the array response results in distinct clustering indicating the suitability of the arrays for distinguishing these analytes. Importantly, the nanointaglio process used here is capable of producing lipid gratings out of different materials with sufficiently uniform heights for the fabrication of an optical nose. PMID:26308001

Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues

NASA Astrophysics Data System (ADS)

Liao, Zhijun; Wang, Xinrui; Zeng, Yeting; Zou, Quan

2016-12-01

The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.

Isolation and characterization of the small subunit of the uptake hydrogenase from the cyanobacterium Nostoc punctiforme.

PubMed

Raleiras, Patrícia; Kellers, Petra; Lindblad, Peter; Styring, Stenbjörn; Magnuson, Ann

2013-06-21

In nitrogen-fixing cyanobacteria, hydrogen evolution is associated with hydrogenases and nitrogenase, making these enzymes interesting targets for genetic engineering aimed at increased hydrogen production. Nostoc punctiforme ATCC 29133 is a filamentous cyanobacterium that expresses the uptake hydrogenase HupSL in heterocysts under nitrogen-fixing conditions. Little is known about the structural and biophysical properties of HupSL. The small subunit, HupS, has been postulated to contain three iron-sulfur clusters, but the details regarding their nature have been unclear due to unusual cluster binding motifs in the amino acid sequence. We now report the cloning and heterologous expression of Nostoc punctiforme HupS as a fusion protein, f-HupS. We have characterized the anaerobically purified protein by UV-visible and EPR spectroscopies. Our results show that f-HupS contains three iron-sulfur clusters. UV-visible absorption of f-HupS has bands ∼340 and 420 nm, typical for iron-sulfur clusters. The EPR spectrum of the oxidized f-HupS shows a narrow g = 2.023 resonance, characteristic of a low-spin (S = ½) [3Fe-4S] cluster. The reduced f-HupS presents complex EPR spectra with overlapping resonances centered on g = 1.94, g = 1.91, and g = 1.88, typical of low-spin (S = ½) [4Fe-4S] clusters. Analysis of the spectroscopic data allowed us to distinguish between two species attributable to two distinct [4Fe-4S] clusters, in addition to the [3Fe-4S] cluster. This indicates that f-HupS binds [4Fe-4S] clusters despite the presence of unusual coordinating amino acids. Furthermore, our expression and purification of what seems to be an intact HupS protein allows future studies on the significance of ligand nature on redox properties of the iron-sulfur clusters of HupS.

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

PubMed Central

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

1998-01-01

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

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

PubMed

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

2010-12-30

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

Methanogenic pathways in Alaskan peatlands at different trophic levels with evidence from stable isotope ratios and metagenomics

NASA Astrophysics Data System (ADS)

Zhang, L.; Liu, X.; Langford, L.; Chanton, J.; Roth, S.; Schaefer, J.; Barkay, T.; Hines, M. E.

2017-12-01

To better constrain the large uncertainties in emission fluxes, it is necessary to improve the understanding of methanogenic pathways in northern peatlands with heterogeneous surface vegetation and pH. Surface vegetation is an excellent indicator of porewater pH, which heavily influences the microbial communities in peatlands. Stable C isotope ratios (d13C) have been used as a robust tool to distinguish methanogenic pathways, especially in conjunction with metagenomic analysis of the microbial communities. To link surface vegetation species compositions, pH, microbial communities, and methanogenic pathways, 15 peatland sites were studied in Fairbanks and Anchorage, Alaska in the summer of 2014. These sites were ordinated using multiple factor analysis into 3 clusters based on pH, temp, CH4 and volatile fatty acid production rates, d13C values, and surface vegetation composition. In the ombrotrophic group (pH 3.3), various Sphagna species dominanted, but included shrubs Ledum decumbens and Eriophorum vaginatum. Primary fermentation rates were slow with no CH4 detected. The fen cluster (pH 5.3) was dominated by various Carex species, and CH4 production rates were lower than those in the intermediate cluster but more enriched in 13C (-49‰). Methanosaeta and Methanosarcina were the dominant methanogens. In the intermediate trophic level (pH 4.7), Sphagnum squarrosum and Carex aquatilis were abundant. The same methanogens as in fen cluster also dominated this group, but with higher abundances, which, in part, lead to the higher CH4 production rates in this cluster. The syntrophs Syntrophobacter and Pelobacter were also more abundant than the fen sites, which may explain the d13CH4 values that were the lighetest among the three clusters (-54‰). The high methanogenic potential in the intermediate trophic sites warrant further study since they are not only present in large areas currently, but also represent the transient stage during the evolution from bog to fen in projected climate change scenarios.

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

PubMed

Gupta, Mayetri; Cheung, Ching-Lung; Hsu, Yi-Hsiang; Demissie, Serkalem; Cupples, L Adrienne; Kiel, Douglas P; Karasik, David

2011-06-01

Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes--SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk. Copyright © 2011 American Society for Bone and Mineral Research.

Constrained clusters of gene expression profiles with pathological features.

PubMed

Sese, Jun; Kurokawa, Yukinori; Monden, Morito; Kato, Kikuya; Morishita, Shinichi

2004-11-22

Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.

Nuclear resonance vibrational spectroscopy reveals the FeS cluster composition and active site vibrational properties of an O 2-tolerant NAD +-reducing [NiFe] hydrogenase

DOE PAGES

Lauterbach, Lars; Wang, Hongxin; Horch, Marius; ...

2014-10-30

Hydrogenases are complex metalloenzymes that catalyze the reversible splitting of molecular hydrogen into protons and electrons essentially without overpotential. The NAD+-reducing soluble hydrogenase (SH) from Ralstonia eutropha is capable of H 2 conversion even in the presence of usually toxic dioxygen. The molecular details of the underlying reactions are largely unknown, mainly because of limited knowledge of the structure and function of the various metal cofactors present in the enzyme. Here, all iron-containing cofactors of the SH were investigated by 57Fe specific nuclear resonance vibrational spectroscopy (NRVS). Our data provide experimental evidence for one [2Fe2S] center and four [4Fe4S] clusters,more » which is consistent with the amino acid sequence composition. Only the [2Fe2S] cluster and one of the four [4Fe4S] clusters were reduced upon incubation of the SH with NADH. This finding explains the discrepancy between the large number of FeS clusters and the small amount of FeS cluster-related signals as detected by electron paramagnetic resonance spectroscopic analysis of several NAD+-reducing hydrogenases. For the first time, Fe–CO and Fe–CN modes derived from the [NiFe] active site could be distinguished by NRVS through selective 13C labeling of the CO ligand. This strategy also revealed the molecular coordinates that dominate the individual Fe–CO modes. The present approach explores the complex vibrational signature of the Fe–S clusters and the hydrogenase active site, thereby showing that NRVS represents a powerful tool for the elucidation of complex biocatalysts containing multiple cofactors.« less

Nuclear resonance vibrational spectroscopy reveals the FeS cluster composition and active site vibrational properties of an O2-tolerant NAD+-reducing [NiFe] hydrogenase.

PubMed

Lauterbach, Lars; Wang, Hongxin; Horch, Marius; Gee, Leland B; Yoda, Yoshitaka; Tanaka, Yoshihito; Zebger, Ingo; Lenz, Oliver; Cramer, Stephen P

Hydrogenases are complex metalloenzymes that catalyze the reversible splitting of molecular hydrogen into protons and electrons essentially without overpotential. The NAD + -reducing soluble hydrogenase (SH) from Ralstonia eutropha is capable of H 2 conversion even in the presence of usually toxic dioxygen. The molecular details of the underlying reactions are largely unknown, mainly because of limited knowledge of the structure and function the various metal cofactors present in the enzyme. Here all iron-containing cofactors of the SH were investigated by 57 Fe specific nuclear resonance vibrational spectroscopy (NRVS). Our data provide experimental evidence for one [2Fe2S] center and four [4Fe4S] clusters, which is consistent with amino acid sequence composition. Only the [2Fe2S] cluster and one of the four [4Fe4S] clusters were reduced upon incubation of the SH with NADH. This finding explains the discrepancy between the large number of FeS clusters and the small amount of FeS cluster-related signals as detected by electron paramagnetic resonance spectroscopic analysis of several NAD + -reducing hydrogenases. For the first time, Fe-CO and Fe-CN modes derived from the [NiFe] active site could be distinguished by NRVS through selective 13 C labeling of the CO ligand. This strategy also revealed the molecular coordinates that dominate the individual Fe-CO modes. The present approach explores the complex vibrational signature of the Fe-S clusters and the hydrogenase active site, thereby showing that NRVS represents a powerful tool for the elucidation of complex biocatalysts containing multiple cofactors.

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 modulation characteristics. Measurements and simulations will better elucidate potential plan-class specific dosimetry correction factors.« less

The interest of gait markers in the identification of subgroups among fibromyalgia patients.

PubMed

Auvinet, Bernard; Chaleil, Denis; Cabane, Jean; Dumolard, Anne; Hatron, Pierre; Juvin, Robert; Lanteri-Minet, Michel; Mainguy, Yves; Negre-Pages, Laurence; Pillard, Fabien; Riviere, Daniel; Maugars, Yves-Michel

2011-11-11

Fibromyalgia (FM) is a heterogeneous syndrome and its classification into subgroups calls for broad-based discussion. FM subgrouping, which aims to adapt treatment according to different subgroups, relies in part, on psychological and cognitive dysfunctions. Since motor control of gait is closely related to cognitive function, we hypothesized that gait markers could be of interest in the identification of FM patients' subgroups. This controlled study aimed at characterizing gait disorders in FM, and subgrouping FM patients according to gait markers such as stride frequency (SF), stride regularity (SR), and cranio-caudal power (CCP) which measures kinesia. A multicentre, observational open trial enrolled patients with primary FM (44.1 ± 8.1 y), and matched controls (44.1 ± 7.3 y). Outcome measurements and gait analyses were available for 52 pairs. A 3-step statistical analysis was carried out. A preliminary single blind analysis using k-means cluster was performed as an initial validation of gait markers. Then in order to quantify FM patients according to psychometric and gait variables an open descriptive analysis comparing patients and controls were made, and correlations between gait variables and main outcomes were calculated. Finally using cluster analysis, we described subgroups for each gait variable and looked for significant differences in self-reported assessments. SF was the most discriminating gait variable (73% of patients and controls). SF, SR, and CCP were different between patients and controls. There was a non-significant association between SF, FIQ and physical components from Short-Form 36 (p = 0.06). SR was correlated to FIQ (p = 0.01) and catastrophizing (p = 0.05) while CCP was correlated to pain (p = 0.01). The SF cluster identified 3 subgroups with a particular one characterized by normal SF, low pain, high activity and hyperkinesia. The SR cluster identified 2 distinct subgroups: the one with a reduced SR was distinguished by high FIQ, poor coping and altered affective status. Gait analysis may provide additional information in the identification of subgroups among fibromyalgia patients. Gait analysis provided relevant information about physical and cognitive status, and pain behavior. Further studies are needed to better understand gait analysis implications in FM.

The interest of gait markers in the identification of subgroups among fibromyalgia patients

PubMed Central

2011-01-01

Background Fibromyalgia (FM) is a heterogeneous syndrome and its classification into subgroups calls for broad-based discussion. FM subgrouping, which aims to adapt treatment according to different subgroups, relies in part, on psychological and cognitive dysfunctions. Since motor control of gait is closely related to cognitive function, we hypothesized that gait markers could be of interest in the identification of FM patients' subgroups. This controlled study aimed at characterizing gait disorders in FM, and subgrouping FM patients according to gait markers such as stride frequency (SF), stride regularity (SR), and cranio-caudal power (CCP) which measures kinesia. Methods A multicentre, observational open trial enrolled patients with primary FM (44.1 ± 8.1 y), and matched controls (44.1 ± 7.3 y). Outcome measurements and gait analyses were available for 52 pairs. A 3-step statistical analysis was carried out. A preliminary single blind analysis using k-means cluster was performed as an initial validation of gait markers. Then in order to quantify FM patients according to psychometric and gait variables an open descriptive analysis comparing patients and controls were made, and correlations between gait variables and main outcomes were calculated. Finally using cluster analysis, we described subgroups for each gait variable and looked for significant differences in self-reported assessments. Results SF was the most discriminating gait variable (73% of patients and controls). SF, SR, and CCP were different between patients and controls. There was a non-significant association between SF, FIQ and physical components from Short-Form 36 (p = 0.06). SR was correlated to FIQ (p = 0.01) and catastrophizing (p = 0.05) while CCP was correlated to pain (p = 0.01). The SF cluster identified 3 subgroups with a particular one characterized by normal SF, low pain, high activity and hyperkinesia. The SR cluster identified 2 distinct subgroups: the one with a reduced SR was distinguished by high FIQ, poor coping and altered affective status. Conclusion Gait analysis may provide additional information in the identification of subgroups among fibromyalgia patients. Gait analysis provided relevant information about physical and cognitive status, and pain behavior. Further studies are needed to better understand gait analysis implications in FM. PMID:22078002

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

PubMed

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

2017-07-14

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

Mpc-scale diffuse radio emission in two massive cool-core clusters of galaxies

NASA Astrophysics Data System (ADS)

Sommer, Martin W.; Basu, Kaustuv; Intema, Huib; Pacaud, Florian; Bonafede, Annalisa; Babul, Arif; Bertoldi, Frank

2017-04-01

Radio haloes are diffuse synchrotron sources on scales of ˜1 Mpc that are found in merging clusters of galaxies, and are believed to be powered by electrons re-accelerated by merger-driven turbulence. We present measurements of extended radio emission on similarly large scales in two clusters of galaxies hosting cool cores: Abell 2390 and Abell 2261. The analysis is based on interferometric imaging with the Karl G. Jansky Very Large Array, Very Large Array and Giant Metrewave Radio Telescope. We present detailed radio images of the targets, subtract the compact emission components and measure the spectral indices for the diffuse components. The radio emission in A2390 extends beyond a known sloshing-like brightness discontinuity, and has a very steep in-band spectral slope at 1.5 GHz that is similar to some known ultrasteep spectrum radio haloes. The diffuse signal in A2261 is more extended than in A2390 but has lower luminosity. X-ray morphological indicators, derived from XMM-Newton X-ray data, place these clusters in the category of relaxed or regular systems, although some asymmetric features that can indicate past minor mergers are seen in the X-ray brightness images. If these two Mpc-scale radio sources are categorized as giant radio haloes, they question the common assumption of radio haloes occurring exclusively in clusters undergoing violent merging activity, in addition to commonly used criteria for distinguishing between radio haloes and minihaloes.

Investigating a cluster of Legionnaires' cases: public health implications.

PubMed

Carr, R; Warren, R; Towers, L; Bartholomew, A; Duggal, H V; Rehman, Y; Harrison, T G; Olowokure, B

2010-06-01

To describe the multidisciplinary investigation and management of a rapidly increasing number of cases of Legionnaires' disease in the North Shropshire area, UK during August 2006. Epidemiological and environmental investigation of a cluster of cases of Legionnaires' disease. Outbreak investigation included: agreeing case definitions; case finding; epidemiological survey; identification and environmental investigation of potential sources; microbiological analysis of clinical and environmental samples; mapping the location of potential sources; and the movement and residence of cases. Three cases of Legionnaires' disease were admitted to a local hospital between 30 and 31 August 2006. Two of these cases were Shropshire residents, with the third living in Wales. A fourth case was also identified which, it was thought, may have been linked to this cluster as the patient had a history of travel to the same area as the two Shropshire residents. Over the next few weeks, three more cases were identified, two of whom were admitted to hospital. Subsequent detailed environmental, epidemiological and microbiological investigation did not support the hypothesis that any of these cases could be linked to a common source. The results of this investigation strongly suggest that a single source was not responsible for the cluster, and it was concluded that this incident was a pseudo-outbreak. This investigation serves as a reminder that clusters can and do occur, and that an apparent outbreak may be a collection of sporadic cases distinguishable only by rigorous epidemiological, environmental and microbiological investigation. Copyright 2010. Published by Elsevier Ltd.

Effusion cytomorphology of small round cell tumors.

PubMed

Ikeda, Katsuhide; Tsuta, Koji

2016-01-01

Small round cell tumors (SRCTs) are a group of tumors composed of small, round, and uniform cells with high nuclear/cytoplasmic (N/C) ratios. The appearance of SRCT neoplastic cells in the effusion fluid is very rare. We reported the cytomorphological findings of SRCTs in effusion cytology, and performed statistical and mathematical analyses for a purpose to distinguish SRCTs. We analyzed the cytologic findings of effusion samples from 40 SRCT cases and measured the lengths of the nuclei, cytoplasms, and the cell cluster areas. The SRCT cases included 14 Ewing sarcoma (EWS)/primitive neuroectodermal tumor cases, 5 synovial sarcoma cases, 6 rhabdomyosarcoma cases, 9 small cell lung carcinoma (SCLC) cases, and 6 diffuse large B-cell lymphoma (DLBL) cases. Morphologically, there were no significant differences in the nuclear and cytoplasmic lengths in cases of EWS, synovial sarcoma, and rhabdomyosarcoma. The cytoplasmic lengths in cases of SCLC and DLBL were smaller than those of EWS, synovial sarcoma, and rhabdomyosarcoma. The nuclear density of the cluster in SCLC was higher than that in other SRCTs, and cases of DLBL showed a lack of anisokaryosis and anisocytosis. We believe that it might be possible to diagnose DLBL and SCLC from cytologic analysis of effusion samples but it is very difficult to use this method to distinguish EWS, synovial sarcoma, and rhabdomyosarcoma. Statistical and mathematical analyses indicated that nuclear density and dispersion of nuclear and cytoplasmic sizes are useful adjuncts to conventional cytologic diagnostic criteria, which are acquired from experience.

MACS: The impact of environment on galaxy evolution at z>0.5

NASA Astrophysics Data System (ADS)

Ma, Cheng-Jiun

2010-08-01

In order to investigate galaxy evolution in environments of greatly varying density, we conduct an extensive spectroscopic survey of galaxies in eight X-ray luminous clusters at redshift higher than 0.5. Unlike most spectroscopic surveys of cluster galaxies, we sample the galaxy population beyond the virial radius of each cluster (out to ˜6 Mpc), thereby probing regions that differ by typically two orders of magnitude in galaxy density. Galaxies are classified by spectroscopic type into emission-line, absorption-line, post starburst (E+A), and starburst (e(a) and e(b)) galaxies, and the spatial distribution of each type is used as a diagnostic of the presence and efficiency of different physical mechanisms of galaxy evolution. Our analysis yields the perhaps strongest confirmation so far of the morphology-density relation for emission- and absorption-line galaxies. In addition, we find E+A galaxies to be exclusively located within the ram-pressure stripping radius of each cluster. Taking advantage of this largest sample of E+A galaxies in clusters compiled to date, the spatial profile of the distribution of E+A galaxies can be studied for the first time. We show that ram-pressure stripping is the dominant, and possibly only, physical mechanism to cause the post-starburst phase of cluster galaxies. In addition, two particular interesting clusters are studied individually. For MACS J0717.5+3745, a clear morphology-density correlation is observed for lenticular (S0) galaxies around this cluster, but becomes insignificant toward the center of cluster. We interpret this finding as evidence of the creation of S0s being triggered primarily in environments of low to intermediate density. In MACS J0025.4-1225, a cluster undergoing a major merger, all faint E+A galaxies are observed to lie near the peak of the X-ray surface brightness, strongly suggesting that starbursts are enhanced as well as terminated during cluster mergers. We conclude that ram-pressure stripping and/or tidal destruction are central to the evolution of galaxies clusters, and that wide-field spectroscopic surveys around clusters are essential to distinguish between competing physical effects driving galaxy evolution in different environments.

Clustering in complex directed networks

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio

2007-08-01

Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient (CC). The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of (binary and weighted) directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph (independently of the direction of their edges) and CCs that only consider particular types of directed triangles (e.g., cycles). The main concepts are illustrated by employing empirical data on world-trade flows.

Infrared spectroscopic probing of dimethylamine clusters in an Ar matrix.

PubMed

Li, Siyang; Kjaergaard, Henrik G; Du, Lin

2016-02-01

Amines have many atmospheric sources and their clusters play an important role in aerosol nucleation processes. Clusters of a typical amine, dimethylamine (DMA), of different sizes were measured with matrix isolation IR (infrared) and NIR (near infrared) spectroscopy. The NIR vibrations are more separated and therefore it is easier to distinguish different sizes of clusters in this region. The DMA clusters, up to DMA tetramer, have been optimized using density functional methods, and the geometries, binding energies and thermodynamic properties of DMA clusters were obtained. The computed frequencies and intensities of NH-stretching vibrations in the DMA clusters were used to interpret the experimental spectra. We have identified the fundamental transitions of the bonded NH-stretching vibration and the first overtone transitions of the bonded and free NH-stretching vibration in the DMA clusters. Based on the changes in vibrational intensities during the annealing processes, the growth of clusters was clearly observed. The results of annealing processes indicate that DMA molecules tend to form larger clusters with lower energies under matrix temperatures, which is also supported by the calculated reaction energies of cluster formation. Copyright © 2015. Published by Elsevier B.V.

Exploiting visual search theory to infer social interactions

NASA Astrophysics Data System (ADS)

Rota, Paolo; Dang-Nguyen, Duc-Tien; Conci, Nicola; Sebe, Nicu

2013-03-01

In this paper we propose a new method to infer human social interactions using typical techniques adopted in literature for visual search and information retrieval. The main piece of information we use to discriminate among different types of interactions is provided by proxemics cues acquired by a tracker, and used to distinguish between intentional and casual interactions. The proxemics information has been acquired through the analysis of two different metrics: on the one hand we observe the current distance between subjects, and on the other hand we measure the O-space synergy between subjects. The obtained values are taken at every time step over a temporal sliding window, and processed in the Discrete Fourier Transform (DFT) domain. The features are eventually merged into an unique array, and clustered using the K-means algorithm. The clusters are reorganized using a second larger temporal window into a Bag Of Words framework, so as to build the feature vector that will feed the SVM classifier.

Use of an Electronic Tongue System and Fuzzy Logic to Analyze Water Samples

NASA Astrophysics Data System (ADS)

Braga, Guilherme S.; Paterno, Leonardo G.; Fonseca, Fernando J.

2009-05-01

An electronic tongue (ET) system incorporating 8 chemical sensors was used in combination with two pattern recognition tools, namely principal component analysis (PCA) and Fuzzy logic for discriminating/classification of water samples from different sources (tap, distilled and three brands of mineral water). The Fuzzy program exhibited a higher accuracy than the PCA and allowed the ET to classify correctly 4 in 5 types of water. Exception was made for one brand of mineral water which was sometimes misclassified as tap water. On the other hand, the PCA grouped water samples in three clusters, one with the distilled water; a second with tap water and one brand of mineral water, and the third with the other two other brands of mineral water. Samples in the second and third clusters could not be distinguished. Nevertheless, close grouping between repeated tests indicated that the ET system response is reproducible. The potential use of the Fuzzy logic as the data processing tool in combination with an electronic tongue system is discussed.

Application of cluster and discriminant analyses to diagnose lithological heterogeneity of the parent material according to its particle-size distribution

NASA Astrophysics Data System (ADS)

Giniyatullin, K. G.; Valeeva, A. A.; Smirnova, E. V.

2017-08-01

Particle-size distribution in soddy-podzolic and light gray forest soils of the Botanical Garden of Kazan Federal University has been studied. The cluster analysis of data on the samples from genetic soil horizons attests to the lithological heterogeneity of the profiles of all the studied soils. It is probable that they are developed from the two-layered sediments with the upper colluvial layer underlain by the alluvial layer. According to the discriminant analysis, the major contribution to the discrimination of colluvial and alluvial layers is that of the fraction >0.25 mm. The results of canonical analysis show that there is only one significant discriminant function that separates alluvial and colluvial sediments on the investigated territory. The discriminant function correlates with the contents of fractions 0.05-0.01, 0.25-0.05, and >0.25 mm. Classification functions making it possible to distinguish between alluvial and colluvial sediments have been calculated. Statistical assessment of particle-size distribution data obtained for the plow horizons on ten plowed fields within the garden indicates that this horizon is formed from colluvial sediments. We conclude that the contents of separate fractions and their ratios cannot be used as a universal criterion of the lithological heterogeneity. However, adequate combination of the cluster and discriminant analyses makes it possible to give a comprehensive assessment of the lithology of soil samples from data on the contents of sand and silt fractions, which considerably increases the information value and reliability of the results.

A new clustering of antibody CDR loop conformations

PubMed Central

North, Benjamin; Lehmann, Andreas; Dunbrack, Roland L.

2010-01-01

Previous analyses of the complementarity determining regions (CDRs) of antibodies have focused on a small number of “canonical” conformations for each loop. This is primarily the result of the work of Chothia and colleagues, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm using affinity propagation. With this data set of over 300 non-redundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or “L1-11” in our nomenclature) for L1, L2, L3, H1 and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one purely by the presence and positions of Pro residues (L3-9). Thus using the Chothia analysis does not require the complicated set of “structure-determining residues” that is often assumed. Of our 28 CDR-lengths, 15 of them have multiple conformational clusters including ten for which Chothia had only one canonical class. We have a total of 72 clusters for the non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of “bulged” vs. “non-bulged” conformations based on the presence or absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus the earlier analyses have been significantly enhanced by the increased data. We believe the new classification will lead to improved methods for antibody structure prediction and design. PMID:21035459

Inequalities in neighbourhood socioeconomic characteristics: potential evidence-base for neighbourhood health planning

PubMed Central

Odoi, Agricola; Wray, Ron; Emo, Marion; Birch, Stephen; Hutchison, Brian; Eyles, John; Abernathy, Tom

2005-01-01

Background Population health planning aims to improve the health of the entire population and to reduce health inequities among population groups. Socioeconomic factors are increasingly being recognized as major determinants of many aspects of health and causes of health inequities. Knowledge of socioeconomic characteristics of neighbourhoods is necessary to identify their unique health needs and enhance identification of socioeconomically disadvantaged populations. Careful integration of this knowledge into health planning activities is necessary to ensure that health planning and service provision are tailored to unique neighbourhood population health needs. In this study, we identify unique neighbourhood socioeconomic characteristics and classify the neighbourhoods based on these characteristics. Principal components analysis (PCA) of 18 socioeconomic variables was used to identify the principal components explaining most of the variation in socioeconomic characteristics across the neighbourhoods. Cluster analysis was used to classify neighbourhoods based on their socioeconomic characteristics. Results Results of the PCA and cluster analysis were similar but the latter were more objective and easier to interpret. Five neighbourhood types with distinguishing socioeconomic and demographic characteristics were identified. The methodology provides a more complete picture of the neighbourhood socioeconomic characteristics than when a single variable (e.g. income) is used to classify neighbourhoods. Conclusion Cluster analysis is useful for generating neighbourhood population socioeconomic and demographic characteristics that can be useful in guiding neighbourhood health planning and service provision. This study is the first of a series of studies designed to investigate health inequalities at the neighbourhood level with a view to providing evidence-base for health planners, service providers and policy makers to help address health inequity issues at the neighbourhood level. Subsequent studies will investigate inequalities in health outcomes both within and across the neighbourhood types identified in the current study. PMID:16092969

Chemical Polymorphism of Essential Oils of Artemisia vulgaris Growing Wild in Lithuania.

PubMed

Judzentiene, Asta; Budiene, Jurga

2018-02-01

Compositional variability of mugwort (Artemisia vulgaris L.) essential oils has been investigated in the study. Plant material (over ground parts at full flowering stage) was collected from forty-four wild populations in Lithuania. The oils from aerial parts were obtained by hydrodistillation and analyzed by GC(FID) and GC/MS. In total, up to 111 components were determined in the oils. As the major constituents were found: sabinene, 1,8-cineole, artemisia ketone, both thujone isomers, camphor, cis-chrysanthenyl acetate, davanone and davanone B. The compositional data were subjected to statistical analysis. The application of PCA (Principal Component Analysis) and AHC (Agglomerative Hierarchical Clustering) allowed grouping the oils into six clusters. AHC permitted to distinguish an artemisia ketone chemotype, which, to the best of our knowledge, is very scarce. Additionally, two rare cis-chrysanthenyl acetate and sabinene oil types were determined for the plants growing in Lithuania. Besides, davanone was found for the first time as a principal component in mugwort oils. The performed study revealed significant chemical polymorphism of essential oils in mugwort plants native to Lithuania; it has expanded our chemotaxonomic knowledge both of A. vulgaris species and Artemisia genus. © 2018 Wiley-VHCA AG, Zurich, Switzerland.

Random whole metagenomic sequencing for forensic discrimination of soils.

PubMed

Khodakova, Anastasia S; Smith, Renee J; Burgoyne, Leigh; Abarno, Damien; Linacre, Adrian

2014-01-01

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Large-Angular-Scale Clustering as a Clue to the Source of UHECRs

NASA Astrophysics Data System (ADS)

Berlind, Andreas A.; Farrar, Glennys R.

We explore what can be learned about the sources of UHECRs from their large-angular-scale clustering (referred to as their "bias" by the cosmology community). Exploiting the clustering on large scales has the advantage over small-scale correlations of being insensitive to uncertainties in source direction from magnetic smearing or measurement error. In a Cold Dark Matter cosmology, the amplitude of large-scale clustering depends on the mass of the system, with more massive systems such as galaxy clusters clustering more strongly than less massive systems such as ordinary galaxies or AGN. Therefore, studying the large-scale clustering of UHECRs can help determine a mass scale for their sources, given the assumption that their redshift depth is as expected from the GZK cutoff. We investigate the constraining power of a given UHECR sample as a function of its cutoff energy and number of events. We show that current and future samples should be able to distinguish between the cases of their sources being galaxy clusters, ordinary galaxies, or sources that are uncorrelated with the large-scale structure of the universe.

Simulating the Birth of Massive Star Clusters: Is Destruction Inevitable?

NASA Astrophysics Data System (ADS)

Rosen, Anna

2013-10-01

Very early in its operation, the Hubble Space Telescope {HST} opened an entirely new frontier: study of the demographics and properties of star clusters far beyond the Milky Way. However, interpretation of HST's observations has proven difficult, and has led to the development of two conflicting models. One view is that most massive star clusters are disrupted during their infancy by feedback from newly formed stars {i.e., "infant mortality"}, independent of cluster mass or environment. The other model is that most star clusters survive their infancy and are disrupted later by mass-dependent dynamical processes. Since observations at present have failed to discriminate between these views, we propose a theoretical investigation to provide new insight. We will perform radiation-hydrodynamic simulations of the formation of massive star clusters, including for the first time a realistic treatment of the most important stellar feedback processes. These simulations will elucidate the physics of stellar feedback, and allow us to determine whether cluster disruption is mass-dependent or -independent. We will also use our simulations to search for observational diagnostics that can distinguish bound from unbound clusters, and to predict how cluster disruption affects the cluster luminosity function in a variety of galactic environments.

Molecular assessment of microbiota structure and dynamics along mixed olive oil and winery wastewaters biotreatment.

PubMed

Eusébio, Ana; Tacão, Marta; Chaves, Sandra; Tenreiro, Rogério; Almeida-Vara, Elsa

2011-07-01

The major parcel of the degradation occurring along wastewater biotreatments is performed either by the native microbiota or by added microbial inocula. The main aim of this study was to apply two fingerprinting methods, temperature gradient gel electrophoresis (TGGE) and length heterogeneity-PCR (LH-PCR) analysis of 16S rRNA gene fragments, in order to assess the microbiota structure and dynamics during mixed olive oil and winery wastewaters aerobic biotreatment performed in a jet-loop reactor (JLR). Sequence homology analysis showed the presence of bacterial genera Gluconacetobacter, Klebsiella, Lactobacillus, Novosphingobium, Pseudomonas, Prevotella, Ralstonia, Sphingobium and Sphingomonas affiliated with five main phylogenetic groups: alpha-, beta- and gamma-Proteobacteria, Firmicutes and Bacteroidetes. LH-PCR analysis distinguished eight predominant DNA fragments correlated with the samples showing highest performance (COD removal rates of 67 up to 75%). Cluster analysis of both TGGE and LH-PCR fingerprinting profiles established five main clusters, with similarity coefficients higher than 79% (TGGE) and 62% (LH-PCR), and related with hydraulic retention time, indicating that this was the main factor responsible for the shifts in the microbiota structure. Canonical correspondence analysis revealed that changes observed on temperature and O(2) level were also responsible for shifts in microbiota composition. Community level metabolic profile analysis was used to test metabolic activities in samples. Integrated data revealed that the microbiota structure corresponds to bacterial groups with high degradative potential and good suitability for this type of effluents biotreatments.

Assessing genetic divergence in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism markers.

PubMed

Zhang, F; Ge, Y Y; Wang, W Y; Shen, X L; Yu, X Y

2012-12-03

Conventional hybridization and selection techniques have aided the development of new ornamental crop cultivars. However, little information is available on the genetic divergence of bromeliad hybrids. In the present study, we investigated the genetic variability in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism (SRAP) markers. The morphological analysis showed that the putative hybrids were intermediate between both parental species with respect to inflorescence characteristics. The 16 SRAP primer combinations yield 265 bands, among which 154 (57.72%) were polymorphic. The genetic similarity was an average of 0.59 and ranged from 0.21 to 0.87, indicating moderate genetic divergence among the hybrids. The unweighted pair group method with arithmetic average (UPGMA)-based cluster analysis distinguished the hybrids from their parents with a genetic distance coefficient of 0.54. The cophenetic correlation was 0.93, indicating a good fit between the dendrogram and the original distance matrix. The two-dimensional plot from the principal coordinate analysis showed that the hybrids were intermediately dispersed between both parents, corresponding to the results of the UPGMA cluster and the morphological analysis. These results suggest that SRAP markers could help to identify breeders, characterize F(1) hybrids of bromeliads at an early stage, and expedite genetic improvement of bromeliad cultivars.

Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms

NASA Technical Reports Server (NTRS)

Storrie-Lombardi, Michael C.; Hoover, Richard B.

2005-01-01

Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures

Distinguishing Fear Versus Distress Symptomatology in Pediatric OCD.

PubMed

Rozenman, Michelle; Peris, Tara; Bergman, R Lindsey; Chang, Susanna; O'Neill, Joseph; McCracken, James T; Piacentini, John

2017-02-01

Prior research has identified OCD subtypes or "clusters" of symptoms that differentially relate to clinical features of the disorder. Given the high comorbidity between OCD and anxiety, OCD symptom clusters may more broadly associate with fear and/or distress internalizing constructs. This study examines fear and distress dimensions, including physical concerns (fear), separation anxiety (fear), perfectionism (distress), and anxious coping (distress), as predictors of previously empirically-derived OCD symptom clusters in a sample of 215 youth diagnosed with primary OCD (ages 7-17, mean age = 12.25). Self-reported separation fears predicted membership in Cluster 1 (aggressive, sexual, religious, somatic obsessions, and checking compulsions) while somatic/autonomic fears predicted membership in Cluster 2 (symmetry obsessions and ordering, counting, repeating compulsions). Results highlight the diversity of pediatric OCD symptoms and their differential association with fear, suggesting the need to carefully assess both OCD and global fear constructs that might be directly targeted in treatment.

Exploring personality clusters among parents of ED subjects. Relationship with parents' psychopathology, attachment, and family dynamics.

PubMed

Amianto, Federico; Daga, Giovanni Abbate; Bertorello, Antonella; Fassino, Secondo

2013-10-01

Eating disorders are some of the most difficult mental disorders to treat and manage. Family interacts with genetic dispositions and other pathogenic factors, and may influence the outburst, development and outcome of EDs. The present study explores with a cluster analysis the personality traits of parents of ED subjects. One-hundred-eight mothers and 104 fathers were tested with Temperament Character Inventory (TCI), Eating Disorder Inventory-2 (EDI-2), State-Trait Anger Expression Inventory (STAX), Family Assessment Device (FAD), Attachment Style Questionnaire (ASQ), Symptom Questionnaire (SQ), Psychological Well-Being scales (PWB). The cluster distribution of parents based on personality traits was explored. Parents' clusters TCI scores were compared as regards personality, psychopathology, attachment and family features. Cross distribution of temperament and character clusters in mothers and fathers, among couples and ED diagnoses of the daughters was explored. Two clusters of mothers and fathers were identified with temperament clustering. Character traits led to two mothers and three fathers clusters. Mothers temperament cluster 1 (MTC1) correspond to a explosive/adventurous profile, MTC2 to a cautious/passive-dependent profile. Fathers temperament cluster 1 (FTC1) was explosive/methodic, FTC2 was independent/methodic. Character clustering distinguished very immature mothers (MCC1) and majority (65%) of character mature mothers with low self-transcendence (MCC2). A third of fathers was severely immature (FCC1), a third impaired as regards relationships (poor cooperativeness and self-transcendence; FCC2), and one third character mature fathers with low self-transcendence (FCC3). Each cluster evidences specific psychopathology and attachment characteristics. FTC1 was more frequently associated with character immaturity. No significant clusters' cross correlation was found in parental couples. Parents' clusters analyze in depth the univocal picture of prototypical mothers and fathers of EDs. Parents not disturbed as regards personality traits are not exceptions. Since EDs are multifactor disorders family dynamics related to parents' personality may be very relevant or even marginal in their pathogenesis. Conversely, parenting may be negatively influenced by relatively marginal personality malfunctions of parents. The clustering approach to the complexity of personality-related dynamics of ED families improves the picture of ED parents. Psychoeducational, counseling and psychotherapeutic family interventions should consider the specific underlying personality of parents. Copyright © 2013 Elsevier Inc. All rights reserved.

Differentiation of commercial vermiculite based on statistical analysis of bulk chemical data: Fingerprinting vermiculite from Libby, Montana U.S.A

USGS Publications Warehouse

Gunter, M.E.; Singleton, E.; Bandli, B.R.; Lowers, H.A.; Meeker, G.P.

2005-01-01

Major-, minor-, and trace-element compositions, as determined by X-ray fluorescence (XRF) analysis, were obtained on 34 samples of vermiculite to ascertain whether chemical differences exist to the extent of determining the source of commercial products. The sample set included ores from four deposits, seven commercially available garden products, and insulation from four attics. The trace-element distributions of Ba, Cr, and V can be used to distinguish the Libby vermiculite samples from the garden products. In general, the overall composition of the Libby and South Carolina deposits appeared similar, but differed from the South Africa and China deposits based on simple statistical methods. Cluster analysis provided a good distinction of the four ore types, grouped the four attic samples with the Libby ore, and, with less certainty, grouped the garden samples with the South Africa ore.

Analysis of earthquake clustering and source spectra in the Salton Sea Geothermal Field

NASA Astrophysics Data System (ADS)

Cheng, Y.; Chen, X.

2015-12-01

The Salton Sea Geothermal field is located within the tectonic step-over between San Andreas Fault and Imperial Fault. Since the 1980s, geothermal energy exploration has resulted with step-like increase of microearthquake activities, which mirror the expansion of geothermal field. Distinguishing naturally occurred and induced seismicity, and their corresponding characteristics (e.g., energy release) is important for hazard assessment. Between 2008 and 2014, seismic data recorded by a local borehole array were provided public access from CalEnergy through SCEC data center; and the high quality local recording of over 7000 microearthquakes provides unique opportunity to sort out characteristics of induced versus natural activities. We obtain high-resolution earthquake location using improved S-wave picks, waveform cross-correlation and a new 3D velocity model. We then develop method to identify spatial-temporally isolated earthquake clusters. These clusters are classified into aftershock-type, swarm-type, and mixed-type (aftershock-like, with low skew, low magnitude and shorter duration), based on the relative timing of largest earthquakes and moment-release. The mixed-type clusters are mostly located at 3 - 4 km depth near injection well; while aftershock-type clusters and swarm-type clusters also occur further from injection well. By counting number of aftershocks within 1day following mainshock in each cluster, we find that the mixed-type clusters have much higher aftershock productivity compared with other types and historic M4 earthquakes. We analyze detailed spatial variation of 'b-value'. We find that the mixed-type clusters are mostly located within high b-value patches, while large (M>3) earthquakes and other types of clusters are located within low b-value patches. We are currently processing P and S-wave spectra to analyze the spatial-temporal correlation of earthquake stress parameter and seismicity characteristics. Preliminary results suggest that the mixed-type clusters and high b-value patches are spatially correlated with low stress drop earthquakes, indicating high-productivity microearthquakes within low differential stress region, potentially due to deeper injection activities.

"Why some consumers don't care": Heterogeneity in household responses to a food scandal.

PubMed

Rieger, Jörg; Weible, Daniela; Anders, Sven

2017-06-01

In the aftermath of food scandals, household perceptions about the health risks posed by failures in food safety play a central role in determining their mitigating behavior. A stream of literature has shown that factors including media coverage of a scandal, risk perceptions, trust in food safety information, and consumption habits matter. This paper deviates from the standard assumption of a homogeneous response to media information across all households exposed to a food scandal. Instead, we present an innovative multi-method approach to investigate the impacts of household heterogeneity in underlying psychological and behavioral, media usage patterns and consumption habits on poultry demand in the aftermath of the 2011 German dioxin scandal. The analysis employs weekly retail purchase and matching survey data for 6133 households covering pre and post scandal periods. The supplementary survey data elicits household respondent's risk perceptions and risk attitudes, product label and media information behavior. Initial factor and cluster analysis identify household segments based on psychographic and behavioral indicators. We then estimate a correlated random effect Tobit model to account for clustered household responses to quantify the influence of media effects distinguishing between short-term risk mitigation behavior and longer-term habit persistence. Our results confirm significant heterogeneity in household's media-induced risk-mitigation responses to the dioxin scandal across three clusters. However, we find that habit persistence in the form of consumption preferences for the affected products were able to largely compensate for demand-reducing media effects across household clusters. Considering heterogeneity in household's risk mitigation behaviors to food scandals holds implications for policy makers and food industry alike. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reconstructing merger timelines using star cluster age distributions: the case of MCG+08-11-002

NASA Astrophysics Data System (ADS)

Davies, Rebecca L.; Medling, Anne M.; U, Vivian; Max, Claire E.; Sanders, David; Kewley, Lisa J.

2016-05-01

We present near-infrared imaging and integral field spectroscopy of the centre of the dusty luminous infrared galaxy merger MCG+08-11-002, taken using the Near InfraRed Camera 2 (NIRC2) and the OH-Suppressing InfraRed Imaging Spectrograph (OSIRIS) on Keck II. We achieve a spatial resolution of ˜25 pc in the K band, allowing us to resolve 41 star clusters in the NIRC2 images. We calculate the ages of 22/25 star clusters within the OSIRIS field using the equivalent widths of the CO 2.3 μm absorption feature and the Br γ nebular emission line. The star cluster age distribution has a clear peak at ages ≲ 20 Myr, indicative of current starburst activity associated with the final coalescence of the progenitor galaxies. There is a possible second peak at ˜65 Myr which may be a product of the previous close passage of the galaxy nuclei. We fit single and double starburst models to the star cluster age distribution and use Monte Carlo sampling combined with two-sided Kolmogorov-Smirnov tests to calculate the probability that the observed data are drawn from each of the best-fitting distributions. There is a >90 per cent chance that the data are drawn from either a single or double starburst star formation history, but stochastic sampling prevents us from distinguishing between the two scenarios. Our analysis of MCG+08-11-002 indicates that star cluster age distributions provide valuable insights into the timelines of galaxy interactions and may therefore play an important role in the future development of precise merger stage classification systems.

Parenting in emerging adulthood: an examination of parenting clusters and correlates.

PubMed

Nelson, Larry J; Padilla-Walker, Laura M; Christensen, Katherine J; Evans, Cortney A; Carroll, Jason S

2011-06-01

The changing nature of the transition to adulthood in western societies, such as the United States, may be extending the length of time parents are engaged in "parenting" activities. However, little is known about different approaches parents take in their interactions with their emerging-adult children. Hence, this study attempted to identify different clusters of parents based on the extent to which they exhibited both extremes of control (psychological control, punishment, verbal hostility, indulgence) and responsiveness (knowledge, warmth, induction, autonomy granting), and to examine how combinations of parenting were related to emerging adult children's relational and individual outcomes (e.g. parent-child relationship quality, drinking, self-worth, depression). The data were collected from 403 emerging adults (M age = 19.89, SD = 1.78, range = 18-26, 62% female) and at least one of their parents (287 fathers and 317 mothers). Eighty-four percent of participants reported being European American, 6% Asian American, 4% African American, 3% Latino, and 4% reported being of other ethnicities. Data were analyzed using hierarchical cluster analysis, separately for mothers and fathers, and identified three similar clusters of parents which we labeled as uninvolved (low on all aspects of parenting), controlling-indulgent (high on both extremes of control and low on all aspects of responsiveness), and authoritative (high on responsiveness and low on control). A fourth cluster was identified for both mothers and fathers and was labeled as inconsistent for mothers (mothers were above the mean on both extremes of control and on responsiveness) and average for fathers (fathers were at the mean on all eight aspects of parenting). The discussion focuses on how each of these clusters effectively distinguished between child outcomes.

Structural characterization of a magnetic granular system under a time-dependent magnetic field: Voronoi tessellation and multifractal analysis

NASA Astrophysics Data System (ADS)

Moctezuma, R. E.; Arauz-Lara, J. L.; Donado, F.

2018-04-01

The structure of a two-dimensional magnetic granular system was determined by multifractal and Voronoi polygon analysis for a wide range of particle concentrations. Randomizing of the particle motions are produced by applying to the system a time-dependent sinusoidal magnetic field directed along the vertical direction. Both repulsive and attractive short-range interactions between the particles are induced. A direct observation of such system shows qualitatively that, as particle concentration increases, the structure evolves from being liquid-like at low particle concentrations to solid-like at high concentrations. We observe the formation of clusters which are small and weakly bonded and short-lived at low concentrations. Above a threshold particle concentration, clusters grow larger and are more strongly attached. In the system, one can distinguish the mobile particles from the immobile particles belonging to clusters, they can be considered separately as two different phases, a fluid and a solid. We determined the information entropy of the system as a whole and separately from each phase as particle concentration increases. The distribution of the Voronoi polygon areas are well fitted by a two-parameter gamma distribution and we have found that the regularity factor shows a notable change when pieces of the solid phase start to form. The methods we use here show that they can use even when the system is heterogeneous and they provide information when changes start.

Comparative analysis of juice volatiles in selected mandarins, mandarin relatives and other citrus genotypes.

PubMed

Yu, Yuan; Bai, Jinhe; Chen, Chunxian; Plotto, Anne; Baldwin, Elizabeth A; Gmitter, Frederick G

2018-02-01

Citrus fruit flavor is an important attribute prioritized in variety improvement. The present study compared juice volatiles compositions from 13 selected citrus genotypes, including six mandarins (Citrus reticulata), three sour oranges (Citrus aurantium), one blood orange (Citrus sinensis), one lime (Citrus limonia), one Clementine (Citrus clementina) and one satsuma (Citrus unshiu). Large differences were observed with respect to volatile compositions among the citrus genotypes. 'Goutou' sour orange contained the greatest number of volatile compounds and the largest volatile production level. 'Ponkan' mandarin had the smallest number of volatiles and 'Owari' satsuma yielded the lowest volatile production level. 'Goutou' sour orange and 'Moro' blood orange were clearly distinguished from other citrus genotypes based on the analysis of volatile compositions, even though they were assigned into one single group with two other sour oranges by the molecular marker profiles. The clustering analysis based on the aroma volatile compositions was able to differentiate mandarin varieties and natural sub-groups, and was also supported by the molecular marker study. The gas chromatography-mass spectrometry analysis of citrus juice aroma volatiles can be used as a tool to distinguish citrus genotypes and assist in the assessment of future citrus breeding programs. The aroma volatile profiles of the different citrus genotypes and inter-relationships detected among volatile compounds and among citrus genotypes will provide fundamental information on the development of marker-assisted selection in citrus breeding. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Association between ICP pulse waveform morphology and ICP B waves.

PubMed

Kasprowicz, Magdalena; Bergsneider, Marvin; Czosnyka, Marek; Hu, Xiao

2012-01-01

The study aimed to investigate changes in the shape of ICP pulses associated with different patterns of the ICP slow waves (0.5-2.0 cycles/min) during ICP overnight monitoring in hydrocephalus. Four patterns of ICP slow waves were characterized in 44 overnight ICP recordings (no waves - NW, slow symmetrical waves - SW, slow asymmetrical waves - AS, slow waves with plateau phase - PW). The morphological clustering and analysis of ICP pulse (MOCAIP) algorithm was utilized to calculate a set of metrics describing ICP pulse morphology based on the location of three sub-peaks in an ICP pulse: systolic peak (P(1)), tidal peak (P(2)) and dicrotic peak (P(3)). Step-wise discriminant analysis was applied to select the most characteristic morphological features to distinguish between different ICP slow waves. Based on relative changes in variability of amplitudes of P(2) and P(3) we were able to distinguish between the combined groups NW + SW and AS + PW (p < 0.000001). The AS pattern can be differentiated from PW based on respective changes in the mean curvature of P(2) and P(3) (p < 0.000001); however, none of the MOCAIP feature separates between NW and SW. The investigation of ICP pulse morphology associated with different ICP B waves may provide additional information for analysing recordings of overnight ICP.

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

High-accuracy identification of incident HIV-1 infections using a sequence clustering based diversity measure.

PubMed

Xia, Xia-Yu; Ge, Meng; Hsi, Jenny H; He, Xiang; Ruan, Yu-Hua; Wang, Zhi-Xin; Shao, Yi-Ming; Pan, Xian-Ming

2014-01-01

Accurate estimates of HIV-1 incidence are essential for monitoring epidemic trends and evaluating intervention efforts. However, the long asymptomatic stage of HIV-1 infection makes it difficult to effectively distinguish incident infections from chronic ones. Current incidence assays based on serology or viral sequence diversity are both still lacking in accuracy. In the present work, a sequence clustering based diversity (SCBD) assay was devised by utilizing the fact that viral sequences derived from each transmitted/founder (T/F) strain tend to cluster together at early stage, and that only the intra-cluster diversity is correlated with the time since HIV-1 infection. The dot-matrix pairwise alignment was used to eliminate the disproportional impact of insertion/deletions (indels) and recombination events, and so was the proportion of clusterable sequences (Pc) as an index to identify late chronic infections with declined viral genetic diversity. Tested on a dataset containing 398 incident and 163 chronic infection cases collected from the Los Alamos HIV database (last modified 2/8/2012), our SCBD method achieved 99.5% sensitivity and 98.8% specificity, with an overall accuracy of 99.3%. Further analysis and evaluation also suggested its performance was not affected by host factors such as the viral subtypes and transmission routes. The SCBD method demonstrated the potential of sequencing based techniques to become useful for identifying incident infections. Its use may be most advantageous for settings with low to moderate incidence relative to available resources. The online service is available at http://www.bioinfo.tsinghua.edu.cn:8080/SCBD/index.jsp.

Genome-Wide Analysis of Type VI System Clusters and Effectors in Burkholderia Species.

PubMed

Nguyen, Thao Thi; Lee, Hyun-Hee; Park, Inmyoung; Seo, Young-Su

2018-02-01

Type VI secretion system (T6SS) has been discovered in a variety of gram-negative bacteria as a versatile weapon to stimulate the killing of eukaryotic cells or prokaryotic competitors. Type VI secretion effectors (T6SEs) are well known as key virulence factors for important pathogenic bacteria. In many Burkholderia species, T6SS has evolved as the most complicated secretion pathway with distinguished types to translocate diverse T6SEs, suggesting their essential roles in this genus. Here we attempted to detect and characterize T6SSs and potential T6SEs in target genomes of plant-associated and environmental Burkholderia species based on computational analyses. In total, 66 potential functional T6SS clusters were found in 30 target Burkholderia bacterial genomes, of which 33% possess three or four clusters. The core proteins in each cluster were specified and phylogenetic trees of three components (i.e., TssC, TssD, TssL) were constructed to elucidate the relationship among the identified T6SS clusters. Next, we identified 322 potential T6SEs in the target genomes based on homology searches and explored the important domains conserved in effector candidates. In addition, using the screening approach based on the profile hidden Markov model (pHMM) of T6SEs that possess markers for type VI effectors (MIX motif) (MIX T6SEs), 57 revealed proteins that were not included in training datasets were recognized as novel MIX T6SE candidates from the Burkholderia species. This approach could be useful to identify potential T6SEs from other bacterial genomes.

Unsupervised tattoo segmentation combining bottom-up and top-down cues

NASA Astrophysics Data System (ADS)

Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

2011-06-01

Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.

Photonuclear reaction as a probe for α -clustering nuclei in the quasi-deuteron region

NASA Astrophysics Data System (ADS)

Huang, B. S.; Ma, Y. G.; He, W. B.

2017-03-01

Photon-nuclear reaction in a transport model frame, namely an extended quantum molecular dynamics model, has been realized at the photon energy of 70-140 MeV in the quasi-deuteron regime. For an important application, we pay a special focus on photonuclear reactions of 12C(γ ,n p )10B where 12C is considered as different configurations including α clustering. Obvious differences for some observables have been observed among different configurations, which can be attributed to spatial-momentum correlation of a neutron-proton pair inside nucleus, and therefore it gives us a sensitive probe to distinguish the different configurations including α clustering with the help of the photonuclear reaction mechanism.

Relationship between Trophic Status and Methanogenic Pathways in Alaskan Peatlands

NASA Astrophysics Data System (ADS)

Zhang, L.; Liu, X.; Sidelinger, W.; Wang, Y.; Hines, M. E.; Langford, L.; Chanton, J.

2015-12-01

To improve predictions of naturally emitted CH4 from northern wetlands, it is necessary to further examine the methanogenic pathways in these wetlands. Stable isotope C ratios (δ13C) have been used as a robust tool to distinguish different pathways, but different sources of parent compounds (acetate and CO2) with unique δ13C may add complexity to previously established criteria. Large portions of peatlands accommodate a mixture of different sphagna and sedges. Plant species may look very similar and belong to the same genus but are different morphologically and physiologically. To better understand the relationships between surface vegetation patterns and methanogenic pathways, 26 peatland sites were studied in Fairbanks and Anchorage, Alaska in summers of 2014 and 2015. These sites were ordinated using multiple factor analysis into 3 clusters based on pH, temp, CH4 and volatile fatty acids production rates, δ13C values, and surface vegetation species/pattern. In the low-pH trophic cluster (pH~3.5), non-vascular/vascular plant ratios (NV/V) were ~ 0.87 and dominated by diverse Sphagnum species and specific sedges (Eriophorum vaginatum), and fermentation was the dominant end-point in decomposition with no CH4 detected. Although NV/V is about the same in the intermediate cluster (0.74) (pH~4.5), and Sphagnum squarrosum was largely present, both hydrogenotrophic (HM) and acetoclastic methanogenesis (AM) were very active. Syntrophy was present at certain sites, which may provide CO2 with unique δ13C for CH4 production. At the highest pH trophic cluster examined in this study (pH~5), non-vascular plants were almost not existent and Carex aquatilis dominated. CH4 production rates (mainly HM) were slower than those in the intermediate cluster and the apparent fractionation factor a was lower than in the sites with syntrophy, which warrants further investigation of the position and compound specific δ13C analysis of volatile fatty acids.

EVIDENCE FOR AN ACCRETION ORIGIN FOR THE OUTER HALO GLOBULAR CLUSTER SYSTEM OF M31

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

Mackey, A. D.; Huxor, A. P.; Ferguson, A. M. N.

2010-07-01

We use a sample of newly discovered globular clusters from the Pan-Andromeda Archaeological Survey (PAndAS) in combination with previously cataloged objects to map the spatial distribution of globular clusters in the M31 halo. At projected radii beyond {approx}30 kpc, where large coherent stellar streams are readily distinguished in the field, there is a striking correlation between these features and the positions of the globular clusters. Adopting a simple Monte Carlo approach, we test the significance of this association by computing the probability that it could be due to the chance alignment of globular clusters smoothly distributed in the M31 halo.more » We find that the likelihood of this possibility is low, below 1%, and conclude that the observed spatial coincidence between globular clusters and multiple tidal debris streams in the outer halo of M31 reflects a genuine physical association. Our results imply that the majority of the remote globular cluster system of M31 has been assembled as a consequence of the accretion of cluster-bearing satellite galaxies. This constitutes the most direct evidence to date that the outer halo globular cluster populations in some galaxies are largely accreted.« less

Genetic relationships of extant brown bears (Ursus arctos) and polar bears (Ursus maritimus).

PubMed

Cronin, Matthew A; MacNeil, Michael D

2012-01-01

Polar bears (Ursus maritimus) and brown bears (Ursus arctos) are closely related species for which extensive mitochondrial and nuclear phylogenetic comparisons have been made. We used previously published genotype data for 8 microsatellite DNA loci from 930 brown bears in 19 populations and 473 polar bears in 16 populations to compare the population genetic relationships of extant populations of the species. Genetic distances (Nei standard distance = 1.157), the proportion of private alleles (52% of alleles are not shared by the species), and Bayesian cluster analysis are consistent with morphological and life-history characteristics that distinguish polar bears and brown bears as different species with little or no gene flow among extant populations.

Evaluation of a Modified Single-Enzyme Amplified-Fragment Length Polymorphism Technique for Fingerprinting and Differentiating of Mycobacterium kansasii Type I Isolates

PubMed Central

Gaafar, Ayman; Josebe Unzaga, M.; Cisterna, Ramón; Clavo, Felicitas Elena; Urra, Elena; Ayarza, Rafael; Martín, Gloria

2003-01-01

The usefulness of single-enzyme amplified-fragment length polymorphism (AFLP) analysis for the subtyping of Mycobacterium kansasii type I isolates was evaluated. This simplified technique classified 253 type I strains into 12 distinct clusters. The discriminating power of this technique was high, and the technique easily distinguished between the epidemiologically unrelated control strains and our clinical isolates. Overall, the technique was relatively rapid and technically simple, yet it gave reproducible and discriminatory results. This technique provides a powerful typing tool which may be helpful in solving many questions concerning the reservoirs, pathogenicities, and modes of transmission of these isolates. PMID:12904399

An introduction to mass cytometry: fundamentals and applications.

PubMed

Tanner, Scott D; Baranov, Vladimir I; Ornatsky, Olga I; Bandura, Dmitry R; George, Thaddeus C

2013-05-01

Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics. The many available enriched stable isotopes of the transition elements can provide up to 100 distinguishable reporting tags, which can be measured simultaneously because of the essential independence of detection provided by the mass spectrometer. We discuss the adaptation of traditional inductively coupled plasma mass spectrometry to cytometry applications. We focus on the generation of cytometry-compatible data and on approaches to unsupervised multivariate clustering analysis. Finally, we provide a high-level review of some recent benchmark reports that highlight the potential for massively multi-parameter mass cytometry.

New Genotypes of Orientia tsutsugamushi Isolated from Humans in Eastern Taiwan

PubMed Central

Lin, Chin-Hui; Chen, Tren-Yi; Chen, Li-Kuang

2012-01-01

Scrub typhus, an acute febrile illness, is caused by the obligate intracellular bacterium Orientia tsutsugamushi. In our study, O. tsutsugamushi was rapidly detected and typed by polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) analysis of the 56-kDa type-specific antigen (TSA) gene. To investigate the genotypes of clinical variants of O. tsutsugamushi, we collected 3223 blood samples from eastern Taiwanese patients with suspected scrub typhus from 2002 to 2008. In total, 505 samples were found to be positive for scrub typhus infection by PCR, and bacteria were isolated from 282 of them. Four prototype genotype strains (Karp, Kato, Kawasaki and Gilliam) and eleven different Taiwanese genotype isolates (Taiwan-A, -B, -C, -D, -E, -G, -H, -J, -N, -O and -P) were identified by RPLF analysis. Taiwan-H, the major genotype in eastern Taiwan, exhibited prevalence and isolation rates of 47.3% (239/505) and 42.6% (120/282), respectively. We also assessed the genetic relatedness of the 56-kDa TSA gene among eight Taiwan-H isolates, thirteen other Taiwanese isolates and 104 DNA sequences deposited in the GenBank database using MEGA version 5.0 and PHYLIP version 3.66. We found that the Taiwan-H isolates formed into a new cluster, which was designated the Taiwan Gilliam-variant (TG-v) cluster to distinguish it from the Japanese Gilliam-variant (JG-v) cluster. According to Simplot analysis, TG-v is a new recombinant strain among Gilliam, Ikeda and Kato. Moreover, the Gilliam-Kawasaki cluster had the highest percentage of RFLP cases and was the most frequently isolated type in eastern Taiwan (50.1%, 253/505; 44.0%, 124/282). These findings shed light on the genetic evolution of O. tsutsugamushi into different strains and may be useful in vaccine development and epidemic disease control in the future. PMID:23071693

Mycoplasma insons sp. nov., a twisted mycoplasma from green iguanas (Iguana iguana)

PubMed Central

May, Meghan; Ortiz, G. Javier; Wendland, Lori D.; Rotstein, David S.; Relich, Ryan F.; Balish, Mitchell F.; Brown, Daniel R.

2008-01-01

Mycoplasma insons sp. nov., first cultured from the choanae and tracheae of healthy green iguanas (Iguana iguana) from El Salvador, was readily distinguished from all previously described mollicutes and assigned to the Mycoplasma fastidiosum phylogenetic cluster by 16S rRNA gene sequence comparisons. Growth inhibition assays distinguished the isolates serologically from the other two members of that cluster. Many M. insons cells exhibit a remarkable twisted rod morphology despite lacking a cell wall. The organism is nonmotile, produces acid from glucose, but does not hydrolyze arginine, esculin, or urea. Mycoplasma insons 16S rRNA gene was also detected by PCR in packed blood cells from culture-negative iguanas. The type strain I17P1T has been deposited with the Mollicutes Collection at Purdue University and with the American Type Culture Collection (ATCC BAA-1435) in the USA. A limited number of cultures generated by the authors have also been deposited with the Culture Collection, University of Göteborg, in Sweden (CCUG 53461). PMID:17697083

Protein-protein interface detection using the energy centrality relationship (ECR) characteristic of proteins.

PubMed

Sudarshan, Sanjana; Kodathala, Sasi B; Mahadik, Amruta C; Mehta, Isha; Beck, Brian W

2014-01-01

Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.

Protein-Protein Interface Detection Using the Energy Centrality Relationship (ECR) Characteristic of Proteins

PubMed Central

Sudarshan, Sanjana; Kodathala, Sasi B.; Mahadik, Amruta C.; Mehta, Isha; Beck, Brian W.

2014-01-01

Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations. PMID:24830938

Neural net applied to anthropological material: a methodical study on the human nasal skeleton.

PubMed

Prescher, Andreas; Meyers, Anne; Gerf von Keyserlingk, Diedrich

2005-07-01

A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.

Observing the clustering properties of galaxy clusters in dynamical dark-energy cosmologies

NASA Astrophysics Data System (ADS)

Fedeli, C.; Moscardini, L.; Bartelmann, M.

2009-06-01

We study the clustering properties of galaxy clusters expected to be observed by various forthcoming surveys both in the X-ray and sub-mm regimes by the thermal Sunyaev-Zel'dovich effect. Several different background cosmological models are assumed, including the concordance ΛCDM and various cosmologies with dynamical evolution of the dark energy. Particular attention is paid to models with a significant contribution of dark energy at early times which affects the process of structure formation. Past light cone and selection effects in cluster catalogs are carefully modeled by realistic scaling relations between cluster mass and observables and by properly taking into account the selection functions of the different instruments. The results show that early dark-energy models are expected to produce significantly lower values of effective bias and both spatial and angular correlation amplitudes with respect to the standard ΛCDM model. Among the cluster catalogs studied in this work, it turns out that those based on eRosita, Planck, and South Pole Telescope observations are the most promising for distinguishing between various dark-energy models.

Toward an empirical definition of pediatric PTSD: the phenomenology of PTSD symptoms in youth.

PubMed

Carrion, Victor G; Weems, Carl F; Ray, Rebecca; Reiss, Allan L

2002-02-01

To examine the frequency and intensity of posttraumatic stress disorder (PTSD) symptoms and their relation to clinical impairment, to examine the requirement of meeting all DSM-IV symptom cluster criteria (i.e., criteria B, C, D), and to examine the aggregation of PTSD symptom clusters across developmental stages. Fifty-nine children between the ages of 7 and 14 years with a history of trauma and PTSD symptoms were assessed with the Clinician-Administered PTSD Scale for Children and Adolescents. Data support the utility of distinguishing between the frequency and the intensity of symptoms in the investigation of the phenomenology of pediatric PTSD. Children fulfilling requirements for two symptom clusters did not differ significantly from children meeting all three cluster criteria with regard to impairment and distress. Reexperience (cluster B) showed increased aggregation with avoidance and numbing (cluster C) and hyperarousal (cluster D) in the later stages of puberty. Frequency and intensity of symptoms may both contribute to the phenomenology of pediatric PTSD. Children with subthreshold criteria for PTSD demonstrate substantial functional impairment and distress.

Atomic dynamics and the problem of the structural stability of free clusters of solidified inert gases

NASA Astrophysics Data System (ADS)

Verkhovtseva, É. T.; Gospodarev, I. A.; Grishaev, A. V.; Kovalenko, S. I.; Solnyshkin, D. D.; Syrkin, E. S.; Feodos'ev, S. B.

2003-05-01

The dependence of the rms amplitudes of atoms in free clusters of solidified inert gases on the cluster size is investigated theoretically and experimentally. Free clusters are produced by homogeneous nucleation in an adiabatically expanding supersonic stream. Electron diffraction is used to measure the rms amplitudes of the atoms; the Jacobi-matrix method is used for theoretical calculations. A series of distinguishing features of the atomic dynamics of microclusters was found. This was necessary to determine the character of the formation and the stability conditions of the crystal structure. It wass shown that for clusters consisting of less than N˜103 atoms, as the cluster size decreases, the rms amplitudes grow much more rapidly than expected from the increase in the specific contribution of the surface. It is also established that an fcc structure of a free cluster, as a rule, contains twinning defects (nuclei of an hcp phase). One reason for the appearance of such defects is the so-called vertex instability (anomalously large oscillation amplitudes) of the atoms in coordination spheres.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Computing the Taxonomic, Morphological and Sexual Variations of Bornean Hornbills (family: Bucerotidae)

NASA Astrophysics Data System (ADS)

Laman, Charlie J. M.; Kho, Angel

Bornean Hornbills (Family Bucerotidae) are omnivorous creatures, distinguished for their large size and large bill. In our study, only five out of eight species of Bornean hornbills were available. Our aims were to determine the taxonomic, morphological and sexual variations, among the species. Nine morphological features were measured from 83 specimens. Canonical Discriminant and Cluster analyses showed that the data were successfully clustered into 5 species. Logistic regression analyses showed that the diagnostic character differentiation is total length. Further results showed that males tend to be bigger than females.

Alternative source models of very low frequency events

NASA Astrophysics Data System (ADS)

Gomberg, J.; Agnew, D. C.; Schwartz, S. Y.

2016-09-01

We present alternative source models for very low frequency (VLF) events, previously inferred to be radiation from individual slow earthquakes that partly fill the period range between slow slip events lasting thousands of seconds and low-frequency earthquakes (LFE) with durations of tenths of a second. We show that VLF events may emerge from bandpass filtering a sum of clustered, shorter duration, LFE signals, believed to be the components of tectonic tremor. Most published studies show VLF events occurring concurrently with tremor bursts and LFE signals. Our analysis of continuous data from Costa Rica detected VLF events only when tremor was also occurring, which was only 7% of the total time examined. Using analytic and synthetic models, we show that a cluster of LFE signals produces the distinguishing characteristics of VLF events, which may be determined by the cluster envelope. The envelope may be diagnostic of a single, dynamic, slowly slipping event that propagates coherently over kilometers or represents a narrowly band-passed version of nearly simultaneous arrivals of radiation from slip on multiple higher stress drop and/or faster propagating slip patches with dimensions of tens of meters (i.e., LFE sources). Temporally clustered LFE sources may be triggered by single or multiple distinct aseismic slip events or represent the nearly simultaneous chance occurrence of background LFEs. Given the nonuniqueness in possible source durations, we suggest it is premature to draw conclusions about VLF event sources or how they scale.

Alternative source models of very low frequency events

USGS Publications Warehouse

Gomberg, Joan S.; Agnew, D.C.; Schwartz, S.Y.

2016-01-01

We present alternative source models for very low frequency (VLF) events, previously inferred to be radiation from individual slow earthquakes that partly fill the period range between slow slip events lasting thousands of seconds and low-frequency earthquakes (LFE) with durations of tenths of a second. We show that VLF events may emerge from bandpass filtering a sum of clustered, shorter duration, LFE signals, believed to be the components of tectonic tremor. Most published studies show VLF events occurring concurrently with tremor bursts and LFE signals. Our analysis of continuous data from Costa Rica detected VLF events only when tremor was also occurring, which was only 7% of the total time examined. Using analytic and synthetic models, we show that a cluster of LFE signals produces the distinguishing characteristics of VLF events, which may be determined by the cluster envelope. The envelope may be diagnostic of a single, dynamic, slowly slipping event that propagates coherently over kilometers or represents a narrowly band-passed version of nearly simultaneous arrivals of radiation from slip on multiple higher stress drop and/or faster propagating slip patches with dimensions of tens of meters (i.e., LFE sources). Temporally clustered LFE sources may be triggered by single or multiple distinct aseismic slip events or represent the nearly simultaneous chance occurrence of background LFEs. Given the nonuniqueness in possible source durations, we suggest it is premature to draw conclusions about VLF event sources or how they scale.

A first constraint on the average mass of ultra-diffuse galaxies from weak gravitational lensing

NASA Astrophysics Data System (ADS)

Sifón, Cristóbal; van der Burg, Remco F. J.; Hoekstra, Henk; Muzzin, Adam; Herbonnet, Ricardo

2018-01-01

The recent discovery of thousands of ultra-diffuse galaxies (UDGs) in nearby galaxy clusters has opened a new window into the process of galaxy formation and evolution. Several scenarios have been proposed to explain the formation history of UDGs, and their ability to survive in the harsh cluster environments. A key requirement to distinguish between these scenarios is a measurement of their halo masses which, due to their low surface brightnesses, has proven difficult if one relies on stellar tracers of the potential. We exploit weak gravitational lensing, a technique that does not depend on these baryonic tracers, to measure the average subhalo mass of 784 UDGs selected in 18 clusters at z ≤ 0.09. Our sample of UDGs has a median stellar mass 〈m⋆〉 = 2 × 108 M⊙ and a median effective radius 〈reff〉 = 2.8 kpc. We constrain the average mass of subhaloes within 30 kpc to log mUDG(r < 30 kpc)/M⊙ ≤ 10.99 at 95 per cent credibility, implying an effective virial mass log m200/M⊙ ≤ 11.80, and a lower limit on the stellar mass fraction within 10 kpc of 1.0 per cent. Such mass is consistent with a simple extrapolation of the subhalo-to-stellar mass relation of typical satellite galaxies in massive clusters. However, our analysis is not sensitive to scatter about this mean mass; the possibility remains that extreme UDGs reside in haloes as massive as the Milky Way.

Univariate and multivariate analysis of tannin-impregnated wood species using vibrational spectroscopy.

PubMed

Schnabel, Thomas; Musso, Maurizio; Tondi, Gianluca

2014-01-01

Vibrational spectroscopy is one of the most powerful tools in polymer science. Three main techniques--Fourier transform infrared spectroscopy (FT-IR), FT-Raman spectroscopy, and FT near-infrared (NIR) spectroscopy--can also be applied to wood science. Here, these three techniques were used to investigate the chemical modification occurring in wood after impregnation with tannin-hexamine preservatives. These spectroscopic techniques have the capacity to detect the externally added tannin. FT-IR has very strong sensitivity to the aromatic peak at around 1610 cm(-1) in the tannin-treated samples, whereas FT-Raman reflects the peak at around 1600 cm(-1) for the externally added tannin. This high efficacy in distinguishing chemical features was demonstrated in univariate analysis and confirmed via cluster analysis. Conversely, the results of the NIR measurements show noticeable sensitivity for small differences. For this technique, multivariate analysis is required and with this chemometric tool, it is also possible to predict the concentration of tannin on the surface.

Genetic differentiation of Colletotrichum gloeosporioides and C. truncatum associated with Anthracnose disease of papaya (Carica papaya L.) and bell pepper (Capsium annuum L.) based on ITS PCR-RFLP fingerprinting.

PubMed

Maharaj, Ariana; Rampersad, Sephra N

2012-03-01

Members of the genus Colletotrichum include some of the most economically important fungal pathogens in the world. Accurate diagnosis is critical to devising disease management strategies. Two species, Colletotrichum gloeosporioides and C. truncatum, are responsible for anthracnose disease in papaya (Carica papaya L.) and bell pepper (Capsicum annuum L.) in Trinidad. The ITS1-5.8S-ITS2 region of 48 Colletotrichum isolates was sequenced, and the ITS PCR products were analyzed by PCR-RFLP analysis. Restriction site polymorphisms generated from 11 restriction enzymes enabled the identification of specific enzymes that were successful in distinguishing between C. gloeosporioides and C. truncatum isolates. Species-specific restriction fragment length polymorphisms generated by the enzymes AluI, HaeIII, PvuII, RsaI, and Sau3A were used to consistently resolve C. gloeosporioides and C. truncatum isolates from papaya. AluI, ApaI, PvuII, RsaI, and SmaI reliably separated isolates of C. gloeosporioides and C. truncatum from bell pepper. PvuII, RsaI, and Sau3A were also capable of distinguishing among the C. gloeosporioides isolates from papaya based on the different restriction patterns that were obtained as a result of intra-specific variation in restriction enzyme recognition sites in the ITS1-5.8S-ITS2 rDNA region. Of all the isolates tested, C. gloeosporioides from papaya also had the highest number of PCR-RFLP haplotypes. Cluster analysis of sequence and PCR-RFLP data demonstrated that all C. gloeosporioides and C. truncatum isolates clustered separately into species-specific clades regardless of host species. Phylograms also revealed consistent topologies which suggested that the genetic distances for PCR-RFLP-generated data were comparable to that of ITS sequence data. ITS PCR-RFLP fingerprinting is a rapid and reliable method to identify and differentiate between Colletotrichum species.

[Differentiation and characterization of yeasts pathogenic for humans (Candida albicans, Exophiala dermatitidis) and algae pathogenic for animals (Prototheca spp.) using Fourier transform infrared spectroscopy (FTIR) in comparison with conventional methods].

PubMed

Schmalreck, A F; Tränkle, P; Vanca, E; Blaschke-Hellmessen, R

1998-01-01

Due to the Fourier-Transform Infrared Spectroscopy (FT-IR) of strain specific traits demonstrated to be a suitable and efficient method for diagnostic and epidemiological determinations for the yeasts Candida albicans, Exophiala dermatitidis and the chlorophylless algae of the genus Prototheca. FT-IR leads in a rapid and economical way to reproducible results according to the spectral differences of intact cells (IR-fingerprints). Different genera, species and sub-species respectively, different strains can be recognized and grouped into different clusters and subclusters. The FT-IR analysis of Candida albicans isolates (n = 150) of 22 newborns-at-risk of an intensive care unit showed, that 86% of the children were colonised with several (2-4) different strains in the oral cavities and faeces. Stationary cross-infections could definitely be determined. Exophiala dermatitidis isolates (n = 31), mostly isolated repetitively within a period of 3 years from sputa of patients suffering from cystic fibrosis could be characterized and grouped patient-specifically over the total sampling period. Of 6 from 8 patients (75%) their individual strains remain the same and could be tracked over the three years. Cross-infections during the stationary treatment could be clearly identified by FT-IR. The Prototheca isolate (n = 43) from live-stock and farm environment showed clear distinguishable clusters differentiating the species P. wickerhamii, P. zopfii and P. stagnora. In addition, the biotypes of P. zopfii could be distinguished, especially the subclusters of variants II and III. It could be demonstrated, that FT-IR is suitable for the routine identification and differentiation of yeasts and algae. However, in spite of the gain of knowledge by using FT-IR for the characterization of microorganisms, the conventional phenotyping and/or genetic analysis of yeast or algae strains cannot be replaced completely. For a final taxonomic classification a combination of conventional methods on FT-IR together with more sophisticated molecular genetic procedures is necessary.

1H-NMR and UPLC-MS metabolomics: Functional tools for exploring chemotypic variation in Sceletium tortuosum from two provinces in South Africa.

PubMed

Zhao, Jianping; Khan, Ikhlas A; Combrinck, Sandra; Sandasi, Maxleene; Chen, Weiyang; Viljoen, Alvaro M

2018-05-17

Sceletium tortuosum (Aizoaceae) is widely recognised for the treatment of stress, anxiety and depression, as well as for obsessive compulsive disorders. A comprehensive intraspecies chemotypic variation study, using samples harvested from two distinct regions of South Africa, was done using both proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy of methanol extracts (N = 145) and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) of acid/base extracts (N = 289). Chemometric analysis of the 1 H-NMR data indicated two main clusters that were region-specific (Northern Cape and Western Cape provinces). Two dimensional (2D) NMR was used to identify analytes that contributed to the clustering as revealed by the S-plot. The sceletium alkaloids, pinitol and two alkylamines, herein reported for the first time from S. tortuosum, were identified as markers that distinguished the two groups. Relative quantification of the marker analytes conducted by qNMR indicated that samples from the Northern Cape generally contained higher concentrations of all the markers than samples from the Western Cape. Quantitative analysis of the four mesembrine alkaloids using a validated UPLC-photo diode array (PDA) detection method confirmed the results of qNMR with regard to the total alkaloid concentrations. Samples from the Northern Cape Province were found to contain, on average, very high total alkaloids, ranging from 4938.0 to 9376.8 mg/kg dry w. Regarding the Western Cape samples, the total yields of the four mesembrine alkaloids were substantially lower (averages 16.4-4143.2 mg/kg). Hierarchical cluster analysis of the UPLC-MS data, based on the alkaloid chemistry, revealed three branches, with one branch comprising samples primarily from the Northern Cape that seemed somewhat chemically conserved, while the other two branches represented mainly samples from the Western Cape. The construction of an orthogonal projections to latent structures-discriminant analysis model and subsequent loadings plot, allowed alkaloid markers to be identified for each cluster. The diverse sceletium alkaloid chemistry of samples from the three clusters may facilitate the recognition of alkaloid profiles, rather than individual compounds, that exert targeted effects on various brain receptors involved in stress, anxiety or depression. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer.

PubMed

Zhang, Cong; Sun, Qian

2017-06-01

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival (PFS) or overall survival (OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that "glycoprotein binding" was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor (PTAFR) and feline Gardner-Rasheed (FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Use of electrospray ionization ion-trap tandem mass spectrometry and principal component analysis to directly distinguish monosaccharides.

PubMed

Xia, Bing; Zhou, Yan; Liu, Xin; Xiao, Juan; Liu, Qing; Gu, Yucheng; Ding, Lisheng

2012-06-15

Carbohydrates are good source of drugs and play important roles in metabolism processes and cellular interactions in organisms. Distinguishing monosaccharide isomers in saccharide derivates is an important and elementary work in investigating saccharides. It is important to develop a fast, simple and direct method for this purpose, which is described in this study. Stock solutions of monosaccharide with a concentration of 400 μM and sodium chloride at a concentration of 10 μM were made in water/methanol (50:50, v/v). The samples were subjected to electrospray ionization ion-trap tandem mass spectrometry (ESI-MS) and the detected [2M + Na - H(2)O](+) ions were further investigated by tandem mass spectrometry (MS/MS), followed by applying principal component analysis (PCA) on the obtained MS/MS data sets. The MS/MS spectra of the [2M + Na - H(2)O](+) ions at m/z 365 for hexoses and m/z 305 for pentoses yielded unambiguous fragment patterns, while rhamnose can be directly identified by its ESI-MS [M + Na](+) ion at m/z 187. PCA showed clustering of MS/MS data of identical monosaccharide samples obtained from different experiments. By using this method, the monosaccharide in daucosterol hydrolysate was successfully identified. A new strategy was developed for differentiation of the monosaccharides using ESI-MS/MS and PCA. In MS/MS spectra, the [2M + Na - H(2)O](+) ions yielded unambiguous distinction. PCA of the archived MS/MS data sets was applied to demonstrate the spatial resolution of the studied samples. This method presented a simple and reliable way for distinguishing monosaccharides by ESI-MS/MS. Copyright © 2012 John Wiley & Sons, Ltd.

Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens

PubMed Central

Thomas, Reuben; Phuong, Jimmy; McHale, Cliona M.; Zhang, Luoping

2012-01-01

We have applied bioinformatic approaches to identify pathways common to chemical leukemogens and to determine whether leukemogens could be distinguished from non-leukemogenic carcinogens. From all known and probable carcinogens classified by IARC and NTP, we identified 35 carcinogens that were associated with leukemia risk in human studies and 16 non-leukemogenic carcinogens. Using data on gene/protein targets available in the Comparative Toxicogenomics Database (CTD) for 29 of the leukemogens and 11 of the non-leukemogenic carcinogens, we analyzed for enrichment of all 250 human biochemical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The top pathways targeted by the leukemogens included metabolism of xenobiotics by cytochrome P450, glutathione metabolism, neurotrophin signaling pathway, apoptosis, MAPK signaling, Toll-like receptor signaling and various cancer pathways. The 29 leukemogens formed 18 distinct clusters comprising 1 to 3 chemicals that did not correlate with known mechanism of action or with structural similarity as determined by 2D Tanimoto coefficients in the PubChem database. Unsupervised clustering and one-class support vector machines, based on the pathway data, were unable to distinguish the 29 leukemogens from 11 non-leukemogenic known and probable IARC carcinogens. However, using two-class random forests to estimate leukemogen and non-leukemogen patterns, we estimated a 76% chance of distinguishing a random leukemogen/non-leukemogen pair from each other. PMID:22851955

Sensory characteristics and consumer preference for chicken meat in Guinea.

PubMed

Sow, T M A; Grongnet, J F

2010-10-01

This study identified the sensory characteristics and consumer preference for chicken meat in Guinea. Five chicken samples [live village chicken, live broiler, live spent laying hen, ready-to-cook broiler, and ready-to-cook broiler (imported)] bought from different locations were assessed by 10 trained panelists using 19 sensory attributes. The ANOVA results showed that 3 chicken appearance attributes (brown, yellow, and white), 5 chicken odor attributes (oily, intense, medicine smell, roasted, and mouth persistent), 3 chicken flavor attributes (sweet, bitter, and astringent), and 8 chicken texture attributes (firm, tender, juicy, chew, smooth, springy, hard, and fibrous) were significantly discriminating between the chicken samples (P<0.05). Principal component analysis of the sensory data showed that the first 2 principal components explained 84% of the sensory data variance. The principal component analysis results showed that the live village chicken, the live spent laying hen, and the ready-to-cook broiler (imported) were very well represented and clearly distinguished from the live broiler and the ready-to-cook broiler. One hundred twenty consumers expressed their preferences for the chicken samples using a 5-point Likert scale. The hierarchical cluster analysis of the preference data identified 4 homogenous consumer clusters. The hierarchical cluster analysis results showed that the live village chicken was the most preferred chicken sample, whereas the ready-to-cook broiler was the least preferred one. The partial least squares regression (PLSR) type 1 showed that 72% of the sensory data for the first 2 principal components explained 83% of the chicken preference. The PLSR1 identified that the sensory characteristics juicy, oily, sweet, hard, mouth persistent, and yellow were the most relevant sensory drivers of the Guinean chicken preference. The PLSR2 (with multiple responses) identified the relationship between the chicken samples, their sensory attributes, and the consumer clusters. Our results showed that there was not a chicken category that was exclusively preferred from the other chicken samples and therefore highlight the existence of place for development of all chicken categories in the local market.

Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis.

PubMed

Buijck, Bianca I; Zuidema, Sytse U; Spruit-van Eijk, Monica; Bor, Hans; Gerritsen, Debby L; Koopmans, Raymond T C M

2012-12-04

Geriatric stroke patients are generally frail, have an advanced age and co-morbidity. It is yet unclear whether specific groups of patients might benefit differently from structured multidisciplinary rehabilitation programs. Therefore, the aims of our study are 1) to determine relevant patient characteristics to distinguish groups of patients based on their admission scores in skilled nursing facilities (SNFs), and (2) to study the course of these particular patient-groups in relation to their discharge destination. This is a longitudinal, multicenter, observational study. We collected data on patient characteristics, balance, walking ability, arm function, co-morbidity, activities of daily living (ADL), neuropsychiatric symptoms, and depressive complaints of 127 geriatric stroke patients admitted to skilled nursing facilities with specific units for geriatric rehabilitation after stroke. Cluster analyses revealed two groups: cluster 1 included patients in poor condition upon admission (n = 52), and cluster 2 included patients in fair/good condition upon admission (n = 75). Patients in both groups improved in balance, walking abilities, and arm function. Patients in cluster 1 also improved in ADL. Depressive complaints decreased significantly in patients in cluster 1 who were discharged to an independent- or assisted-living situation. Compared to 80% of the patients in cluster 2, a lower proportion (46%) of the patients in cluster 1 were discharged to an independent- or assisted-living situation. Stroke patients referred for rehabilitation to SNFs could be clustered on the basis of their condition upon admission. Although patients in poor condition on admission were more likely to be referred to a facility for long-term care, this was certainly not the case in all patients. Almost half of them could be discharged to an independent or assisted living situation, which implied that also in patients in poor condition on admission, discharge to an independent or assisted living situation was an attainable goal. It is important to put substantial effort into the rehabilitation of patients in poor condition at admission.

Combination of multilocus sequence typing and pulsed-field gel electrophoresis reveals an association of molecular clonality with the emergence of extensive-drug resistance (XDR) in Salmonella.

PubMed

Cao, Yongzhong; Shen, Yongxiu; Cheng, Lingling; Zhang, Xiaorong; Wang, Chao; Wang, Yan; Zhou, Xiaohui; Chao, Guoxiang; Wu, Yantao

2018-03-01

Salmonellae is one of the most important foodborne pathogens and becomes resistant to multiple antibiotics, which represents a significant challenge to food industry and public health. However, a molecular signature that can be used to distinguish antimicrobial resistance profile, particularly multi-drug resistance or extensive-drug resistance (XDR). In the current study, 168 isolates from the chicken and pork production chains and ill chickens were characterized by serotyping, antimicrobial susceptibility test, multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE). The results showed that these isolates belonged to 13 serotypes, 14 multilocus sequence types (STs), 94 PFGE genotypes, and 70 antimicrobial resistant profiles. S. Enteritidis, S. Indiana, and S. Derby were the predominant serotypes, corresponding to the ST11, ST17, and ST40 clones, respectively and the PFGE Cluster A, Cluster E, and Cluster D, respectively. Among the ST11-S. Enteritidis (Cluster A) and the ST40-S. Derby (Cluster D) clones, the majority of isolates were resistant to 4-8 antimicrobial agents, whereas in the ST17S. Indiana (Cluster E) clone, isolates showed extensive-drug resistance (XDR) to 9-16 antimicrobial agents. The bla TEM-1-like gene was prevalent in the ST11 and ST17 clones corresponding to high ampicillin resistance. The bla TEM-1-like , bla CTX-M , bla OXA-1-like , sul1, aaC4, aac(6')-1b, dfrA17, and floR gene complex was highly prevalent among isolates of ST17, corresponding to an XDR phenotype. These results demonstrated the association of the resistant phenotypes and genotypes with ST clone and PFGE cluster. Our results also indicated that the newly identified gene complex comprising bla TEM-1-like , bla CTX-M , bla OXA-1-like , sul1, aaC4, aac(6')-1b, dfrA17, and floR, was responsible for the emergence of the ST17S. Indiana XDR clone. ST17 could be potentially used as a molecular signature to distinguish S. Indiana XDR clone. Copyright © 2017 Elsevier GmbH. All rights reserved.

The OGCleaner: filtering false-positive homology clusters.

PubMed

Fujimoto, M Stanley; Suvorov, Anton; Jensen, Nicholas O; Clement, Mark J; Snell, Quinn; Bybee, Seth M

2017-01-01

Detecting homologous sequences in organisms is an essential step in protein structure and function prediction, gene annotation and phylogenetic tree construction. Heuristic methods are often employed for quality control of putative homology clusters. These heuristics, however, usually only apply to pairwise sequence comparison and do not examine clusters as a whole. We present the Orthology Group Cleaner (the OGCleaner), a tool designed for filtering putative orthology groups as homology or non-homology clusters by considering all sequences in a cluster. The OGCleaner relies on high-quality orthologous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish between true-positive and false-positive homology groups. This package aims to improve the quality of phylogenetic tree construction especially in instances of lower-quality transcriptome assemblies. https://github.com/byucsl/ogcleaner CONTACT: sfujimoto@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

No sign (yet) of intergalactic globular clusters in the Local Group

NASA Astrophysics Data System (ADS)

Mackey, A. D.; Beasley, M. A.; Leaman, R.

2016-07-01

We present Gemini Multi-Object Spectrograph (GMOS) imaging of 12 candidate intergalactic globular clusters (IGCs) in the Local Group, identified in a recent survey of the Sloan Digital Sky Survey (SDSS) footprint by di Tullio Zinn & Zinn. Our image quality is sufficiently high, at ˜0.4-0.7 arcsec, that we are able to unambiguously classify all 12 targets as distant galaxies. To reinforce this conclusion we use GMOS images of globular clusters in the M31 halo, taken under very similar conditions, to show that any genuine clusters in the putative IGC sample would be straightforward to distinguish. Based on the stated sensitivity of the di Tullio Zinn & Zinn search algorithm, we conclude that there cannot be a significant number of IGCs with MV ≤ -6 lying unseen in the SDSS area if their properties mirror those of globular clusters in the outskirts of M31 - even a population of 4 would have only a ≈1 per cent chance of non-detection.

Acute Physiological and Thermoregulatory Responses to Extended Interval Training in Endurance Runners: Influence of Athletic Performance and Age

PubMed Central

García-Pinillos, Felipe; Soto-Hermoso, Víctor Manuel; Latorre-Román, Pedro Ángel

2015-01-01

This study aimed to describe the acute impact of extended interval training (EIT) on physiological and thermoregulatory levels, as well as to determine the influence of athletic performance and age effect on the aforementioned response in endurance runners. Thirty-one experienced recreational male endurance runners voluntarily participated in this study. Subjects performed EIT on an outdoor running track, which consisted of 12 runs of 400 m. The rate of perceived exertion, physiological response through the peak and recovery heart rate, blood lactate, and thermoregulatory response through tympanic temperature, were controlled. A repeated measures analysis revealed significant differences throughout EIT in examined variables. Cluster analysis grouped according to the average performance in 400 m runs led to distinguish between athletes with a higher and lower sports level. Cluster analysis was also performed according to age, obtaining an older group and a younger group. The one-way analysis of variance between groups revealed no significant differences (p≥0.05) in the response to EIT. The results provide a detailed description of physiological and thermoregulatory responses to EIT in experienced endurance runners. This allows a better understanding of the impact of a common training stimulus on the physiological level inducing greater accuracy in the training prescription. Moreover, despite the differences in athletic performance or age, the acute physiological and thermoregulatory responses in endurance runners were similar, as long as EIT was performed at similar relative intensity. PMID:26839621

Diversity in Older Adults' Use of the Internet: Identifying Subgroups Through Latent Class Analysis.

PubMed

van Boekel, Leonieke C; Peek, Sebastiaan Tm; Luijkx, Katrien G

2017-05-24

As for all individuals, the Internet is important in the everyday life of older adults. Research on older adults' use of the Internet has merely focused on users versus nonusers and consequences of Internet use and nonuse. Older adults are a heterogeneous group, which may implicate that their use of the Internet is diverse as well. Older adults can use the Internet for different activities, and this usage can be of influence on benefits the Internet can have for them. The aim of this paper was to describe the diversity or heterogeneity in the activities for which older adults use the Internet and determine whether diversity is related to social or health-related variables. We used data of a national representative Internet panel in the Netherlands. Panel members aged 65 years and older and who have access to and use the Internet were selected (N=1418). We conducted a latent class analysis based on the Internet activities that panel members reported to spend time on. Second, we described the identified clusters with descriptive statistics and compared the clusters using analysis of variance (ANOVA) and chi-square tests. Four clusters were distinguished. Cluster 1 was labeled as the "practical users" (36.88%, n=523). These respondents mainly used the Internet for practical and financial purposes such as searching for information, comparing products, and banking. Respondents in Cluster 2, the "minimizers" (32.23%, n=457), reported lowest frequency on most Internet activities, are older (mean age 73 years), and spent the smallest time on the Internet. Cluster 3 was labeled as the "maximizers" (17.77%, n=252); these respondents used the Internet for various activities, spent most time on the Internet, and were relatively younger (mean age below 70 years). Respondents in Cluster 4, the "social users," mainly used the Internet for social and leisure-related activities such as gaming and social network sites. The identified clusters significantly differed in age (P<.001, ω 2 =0.07), time spent on the Internet (P<.001, ω 2 =0.12), and frequency of downloading apps (P<.001, ω 2 =0.14), with medium to large effect sizes. Social and health-related variables were significantly different between the clusters, except social and emotional loneliness. However, effect sizes were small. The minimizers scored significantly lower on psychological well-being, instrumental activities of daily living (iADL), and experienced health compared with the practical users and maximizers. Older adults are a diverse group in terms of their activities on the Internet. This underlines the importance to look beyond use versus nonuse when studying older adults' Internet use. The clusters we have identified in this study can help tailor the development and deployment of eHealth intervention to specific segments of the older population. ©Leonieke C van Boekel, Sebastiaan TM Peek, Katrien G Luijkx. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.05.2017.

Diversity in Older Adults’ Use of the Internet: Identifying Subgroups Through Latent Class Analysis

PubMed Central

van Boekel, Leonieke C; Peek, Sebastiaan TM; Luijkx, Katrien G

2017-01-01

Background As for all individuals, the Internet is important in the everyday life of older adults. Research on older adults’ use of the Internet has merely focused on users versus nonusers and consequences of Internet use and nonuse. Older adults are a heterogeneous group, which may implicate that their use of the Internet is diverse as well. Older adults can use the Internet for different activities, and this usage can be of influence on benefits the Internet can have for them. Objective The aim of this paper was to describe the diversity or heterogeneity in the activities for which older adults use the Internet and determine whether diversity is related to social or health-related variables. Methods We used data of a national representative Internet panel in the Netherlands. Panel members aged 65 years and older and who have access to and use the Internet were selected (N=1418). We conducted a latent class analysis based on the Internet activities that panel members reported to spend time on. Second, we described the identified clusters with descriptive statistics and compared the clusters using analysis of variance (ANOVA) and chi-square tests. Results Four clusters were distinguished. Cluster 1 was labeled as the “practical users” (36.88%, n=523). These respondents mainly used the Internet for practical and financial purposes such as searching for information, comparing products, and banking. Respondents in Cluster 2, the “minimizers” (32.23%, n=457), reported lowest frequency on most Internet activities, are older (mean age 73 years), and spent the smallest time on the Internet. Cluster 3 was labeled as the “maximizers” (17.77%, n=252); these respondents used the Internet for various activities, spent most time on the Internet, and were relatively younger (mean age below 70 years). Respondents in Cluster 4, the “social users,” mainly used the Internet for social and leisure-related activities such as gaming and social network sites. The identified clusters significantly differed in age (P<.001, ω2=0.07), time spent on the Internet (P<.001, ω2=0.12), and frequency of downloading apps (P<.001, ω2=0.14), with medium to large effect sizes. Social and health-related variables were significantly different between the clusters, except social and emotional loneliness. However, effect sizes were small. The minimizers scored significantly lower on psychological well-being, instrumental activities of daily living (iADL), and experienced health compared with the practical users and maximizers. Conclusions Older adults are a diverse group in terms of their activities on the Internet. This underlines the importance to look beyond use versus nonuse when studying older adults’ Internet use. The clusters we have identified in this study can help tailor the development and deployment of eHealth intervention to specific segments of the older population. PMID:28539302

Age, environment, object recognition and morphological diversity of GFAP-immunolabeled astrocytes.

PubMed

Diniz, Daniel Guerreiro; de Oliveira, Marcus Augusto; de Lima, Camila Mendes; Fôro, César Augusto Raiol; Sosthenes, Marcia Consentino Kronka; Bento-Torres, João; da Costa Vasconcelos, Pedro Fernando; Anthony, Daniel Clive; Diniz, Cristovam Wanderley Picanço

2016-10-10

Few studies have explored the glial response to a standard environment and how the response may be associated with age-related cognitive decline in learning and memory. Here we investigated aging and environmental influences on hippocampal-dependent tasks and on the morphology of an unbiased selected population of astrocytes from the molecular layer of dentate gyrus, which is the main target of perforant pathway. Six and twenty-month-old female, albino Swiss mice were housed, from weaning, in a standard or enriched environment, including running wheels for exercise and tested for object recognition and contextual memories. Young adult and aged subjects, independent of environment, were able to distinguish familiar from novel objects. All experimental groups, except aged mice from standard environment, distinguish stationary from displaced objects. Young adult but not aged mice, independent of environment, were able to distinguish older from recent objects. Only young mice from an enriched environment were able to distinguish novel from familiar contexts. Unbiased selected astrocytes from the molecular layer of the dentate gyrus were reconstructed in three-dimensions and classified using hierarchical cluster analysis of bimodal or multimodal morphological features. We found two morphological phenotypes of astrocytes and we designated type I the astrocytes that exhibited significantly higher values of morphological complexity as compared with type II. Complexity = [Sum of the terminal orders + Number of terminals] × [Total branch length/Number of primary branches]. On average, type I morphological complexity seems to be much more sensitive to age and environmental influences than that of type II. Indeed, aging and environmental impoverishment interact and reduce the morphological complexity of type I astrocytes at a point that they could not be distinguished anymore from type II. We suggest these two types of astrocytes may have different physiological roles and that the detrimental effects of aging on memory in mice from a standard environment may be associated with a reduction of astrocytes morphological diversity.

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

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

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE PAGES

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; ...

2015-07-09

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

Statistical classification of hydrogeologic regions in the fractured rock area of Maryland and parts of the District of Columbia, Virginia, West Virginia, Pennsylvania, and Delaware

USGS Publications Warehouse

Fleming, Brandon J.; LaMotte, Andrew E.; Sekellick, Andrew J.

2013-01-01

Hydrogeologic regions in the fractured rock area of Maryland were classified using geographic information system tools with principal components and cluster analyses. A study area consisting of the 8-digit Hydrologic Unit Code (HUC) watersheds with rivers that flow through the fractured rock area of Maryland and bounded by the Fall Line was further subdivided into 21,431 catchments from the National Hydrography Dataset Plus. The catchments were then used as a common hydrologic unit to compile relevant climatic, topographic, and geologic variables. A principal components analysis was performed on 10 input variables, and 4 principal components that accounted for 83 percent of the variability in the original data were identified. A subsequent cluster analysis grouped the catchments based on four principal component scores into six hydrogeologic regions. Two crystalline rock hydrogeologic regions, including large parts of the Washington, D.C. and Baltimore metropolitan regions that represent over 50 percent of the fractured rock area of Maryland, are distinguished by differences in recharge, Precipitation minus Potential Evapotranspiration, sand content in soils, and groundwater contributions to streams. This classification system will provide a georeferenced digital hydrogeologic framework for future investigations of groundwater availability in the fractured rock area of Maryland.

Discrimination of Scedosporium prolificans against Pseudallescheria boydii and Scedosporium apiospermum by semiautomated repetitive sequence-based PCR.

PubMed

Steinmann, J; Schmidt, D; Buer, J; Rath, P-M

2011-07-01

The laboratory identification of Pseudallescheria and Scedosporium isolates at the species level is important for clinical and epidemiological purposes. This study used semiautomated repetitive sequence-based polymerase chain reaction (rep-PCR) to identify Pseudallescheria/Scedosporium. Reference strains of Pseudallescheria boydii (n = 12), Scedosporium prolificans (n = 8), Scedosporium apiospermum (n = 9), and clinical/environmental isolates (P. boydii, 7; S. prolificans, 7; S. apiospermum, 7) were analyzed by rep-PCR. All clinical isolates were identified by morphological and phenotypic characteristics and by sequence analysis. Species identification of reference strains was based on the results of available databases. Rep-PCR studies were also conducted with various molds to differentiate Pseudallescheria/Scedosporium spp. from other commonly encountered filamentous fungi. All tested Pseudallescheria/Scedosporium isolates were distinguishable from the other filamentous fungi. All Scedosporium prolificans strains clustered within the cutoff of 85%, and species identification by rep-PCR showed an agreement of 100% with sequence analysis. However, several isolates of P. boydii and S. apiospermum did not cluster within the 85% cutoff with the same species by rep-PCR. Although the identification of P. boydii and S. apiospermum was not correct, the semiautomated rep-PCR system is a promising tool for the identification of S. prolificans isolates.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task.

PubMed

König, Alexandra; Linz, Nicklas; Tröger, Johannes; Wolters, Maria; Alexandersson, Jan; Robert, Phillipe

2018-06-08

Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline. © 2018 S. Karger AG, Basel.

Efficient evaluation of sampling quality of molecular dynamics simulations by clustering of dihedral torsion angles and Sammon mapping.

PubMed

Frickenhaus, Stephan; Kannan, Srinivasaraghavan; Zacharias, Martin

2009-02-01

A direct conformational clustering and mapping approach for peptide conformations based on backbone dihedral angles has been developed and applied to compare conformational sampling of Met-enkephalin using two molecular dynamics (MD) methods. Efficient clustering in dihedrals has been achieved by evaluating all combinations resulting from independent clustering of each dihedral angle distribution, thus resolving all conformational substates. In contrast, Cartesian clustering was unable to accurately distinguish between all substates. Projection of clusters on dihedral principal component (PCA) subspaces did not result in efficient separation of highly populated clusters. However, representation in a nonlinear metric by Sammon mapping was able to separate well the 48 highest populated clusters in just two dimensions. In addition, this approach also allowed us to visualize the transition frequencies between clusters efficiently. Significantly, higher transition frequencies between more distinct conformational substates were found for a recently developed biasing-potential replica exchange MD simulation method allowing faster sampling of possible substates compared to conventional MD simulations. Although the number of theoretically possible clusters grows exponentially with peptide length, in practice, the number of clusters is only limited by the sampling size (typically much smaller), and therefore the method is well suited also for large systems. The approach could be useful to rapidly and accurately evaluate conformational sampling during MD simulations, to compare different sampling strategies and eventually to detect kinetic bottlenecks in folding pathways.

RNA polymerase beta-subunit gene (rpoB) sequence analysis for the identification of Bacteroides spp.

PubMed

Ko, K S; Kuwahara, T; Haehwa, L; Yoon, Y-J; Kim, B-J; Lee, K-H; Ohnishi, Y; Kook, Y-H

2007-01-01

Partial rpoB sequences (317 bp) of 11 species of Bacteroides, two Porphyromonas spp. and two Prevotella spp. were compared to delineate the genetic relationships among Bacteroides and closely related anaerobic species. The high level of inter-species sequence dissimilarities (7.6-20.8%) allowed the various Bacteroides spp. to be distinguished. The position of the Bacteroides distasonis and Bacteriodes merdae cluster in the rpoB tree was different from the position in the 16S rRNA gene tree. Based on rpoB sequence similarity and clustering in the rpoB tree, it was possible to correctly re-identify 80 clinical isolates of Bacteroides. In addition to two subgroups, cfiA-negative (division I) and cfiA-positive (division II), of Bacteroides fragilis isolates, two distinct subgroups were also found among Bacteroides ovatus and Bacteroides thetaiotaomicron isolates. Bacteroides genus-specific rpoB PCR and B. fragilis species-specific rpoB PCR allowed Bacteroides spp. to be differentiated from Porphyromonas and Prevotella spp., and also allowed B. fragilis to be differentiated from other non-fragilisBacteroides spp. included in the present study.

Submegabase Clusters of Unstable Tandem Repeats Unique to the Tla Region of Mouse T Haplotypes

PubMed Central

Uehara, H.; Ebersole, T.; Bennett, D.; Artzt, K.

1990-01-01

We describe here the identification and genomic organization of mouse t haplotype-specific elements (TSEs) 7.8 and 5.8 kb in length. The TSEs exist as submegabase-long clusters of tandem repeats localized in the Tla region of the major histocompatibility complex of all t haplotype chromosomes examined. In contrast, no such clusters were detected among 12 inbred strains of Mus musculus and other Mus species; thus, clusters of TSEs represent the first absolutely qualitative difference between t haplotypes and wild-type chromosomes. Pulsed field gel electrophoresis shows that the number of clusters, and the number of repeats in each cluster are extremely variable. Dramatic quantitative differences of TSEs uniquely distinguish every independent t haplotype from any other. The complete nucleotide sequence of one 7.8-kb TSE reveals significant homology to the ETn (a major transcript in the early embryo of the mouse), and some homologies to intracisternal A-particles and the mammary tumor virus env gene. Apart from the diagnostic relevance to t haplotypes, evolutionary and functional significances are discussed with respect to chromosome structure and genetic recombination. PMID:2076812

Externalizing disorders: cluster 5 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Krueger, R F; South, S C

2009-12-01

The extant major psychiatric classifications DSM-IV and ICD-10 are purportedly atheoretical and largely descriptive. Although this achieves good reliability, the validity of a medical diagnosis is greatly enhanced by an understanding of the etiology. In an attempt to group mental disorders on the basis of etiology, five clusters have been proposed. We consider the validity of the fifth cluster, externalizing disorders, within this proposal. We reviewed the literature in relation to 11 validating criteria proposed by the Study Group of the DSM-V Task Force, in terms of the extent to which these criteria support the idea of a coherent externalizing spectrum of disorders. This cluster distinguishes itself by the central role of disinhibitory personality in mental disorders spread throughout sections of the current classifications, including substance dependence, antisocial personality disorder and conduct disorder. Shared biomarkers, co-morbidity and course offer additional evidence for a valid cluster of externalizing disorders. Externalizing disorders meet many of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster.

Distinguishing fear versus distress symptomatology in pediatric OCD

PubMed Central

Rozenman, Michelle; Peris, Tara; Bergman, R. Lindsey; Chang, Susanna; O’Neill, Joseph; McCracken, James T.; Piacentini, John

2018-01-01

Prior research has identified OCD subtypes or “clusters” of symptoms that differentially relate to clinical features of the disorder. Given the high comorbidity between OCD and anxiety, OCD symptom clusters may more broadly associate with fear and/or distress internalizing constructs. This study examines fear and distress dimensions, including physical concerns (fear), separation anxiety (fear), perfectionism (distress), and anxious coping (distress), as predictors of previously empirically-derived OCD symptom clusters in a sample of 215 youth diagnosed with primary OCD (ages 7 to 17, mean age = 12.25). Self-reported separation fears predicted membership in Cluster 1 (aggressive, sexual, religious, somatic obsessions, and checking compulsions) while somatic/autonomic fears predicted membership in Cluster 2 (symmetry obsessions and ordering, counting, repeating compulsions). Results highlight the diversity of pediatric OCD symptoms and their differential association with fear, suggesting the need to carefully assess both OCD and global fear constructs that might be directly targeted in treatment. PMID:27225633

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

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the 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. 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.

The population of submillimeter galaxies and its impact on the detection of the Sunyaev-Zel'Dovich Effect

NASA Astrophysics Data System (ADS)

Downes, Thomas Patrick

The population of submillimeter galaxies (SMGs) informs models of the formation of structure in the universe. It is likely that SMGs are the progenitors of systems like the Milky Way because their spectra imply the high star formation rates necessary to form the requisite number of stars. Until recently millimeter/submillimeter experiments did not have the sensitivity to image both wide and deep to distinguish between competing models of galaxy evolution. AzTEC is an array instrument operating at 1.1mm that has the necessary sensitivity and dedicated telescope time and has begun making groundbreaking contributions to the field. Furthermore, SMGs are an expected foreground to the detection of the Sunyaev-Zel'dovich Effect (SZE) in clusters of galaxies. In particular, at wavelengths longer than 1.4mm the positive flux of SMGs below an instrument's sensitivity threshold will counteract the decrement expected from the SZE. The detection of galaxy clusters is a promising approach to understanding the expansion history of the universe and putting constraints on s 8 as well as on the properties the dark energy. We present below the results of an analysis of AzTEC surveys for SMGs in galaxy cluster environments as well as "blank" fields far away from clusters. We compare the two results and those of previous surveys and estimate their implications for the ability of a blind survey at 2mm to detect the SZE in clusters. We find that the noise contributions of SMGs to SZE detection are small compared to the sensitivity of existing surveys, such as that of the South Pole Telescope.

THE EFFECT OF UNRESOLVED BINARIES ON GLOBULAR CLUSTER PROPER-MOTION DISPERSION PROFILES

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

Bianchini, P.; Norris, M. A.; Ven, G. van de

2016-03-20

High-precision kinematic studies of globular clusters (GCs) require an accurate knowledge of all possible sources of contamination. Among other sources, binary stars can introduce systematic biases in the kinematics. Using a set of Monte Carlo cluster simulations with different concentrations and binary fractions, we investigate the effect of unresolved binaries on proper-motion dispersion profiles, treating the simulations like Hubble Space Telescope proper-motion samples. Since GCs evolve toward a state of partial energy equipartition, more-massive stars lose energy and decrease their velocity dispersion. As a consequence, on average, binaries have a lower velocity dispersion, since they are more-massive kinematic tracers. Wemore » show that, in the case of clusters with high binary fractions (initial binary fractions of 50%) and high concentrations (i.e., closer to energy equipartition), unresolved binaries introduce a color-dependent bias in the velocity dispersion of main-sequence stars of the order of 0.1–0.3 km s{sup −1} (corresponding to 1%−6% of the velocity dispersion), with the reddest stars having a lower velocity dispersion, due to the higher fraction of contaminating binaries. This bias depends on the ability to distinguish binaries from single stars, on the details of the color–magnitude diagram and the photometric errors. We apply our analysis to the HSTPROMO data set of NGC 7078 (M15) and show that no effect ascribable to binaries is observed, consistent with the low binary fraction of the cluster. Our work indicates that binaries do not significantly bias proper-motion velocity-dispersion profiles, but should be taken into account in the error budget of kinematic analyses.« less

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

PubMed

Homaeinezhad, M R; Erfanianmoshiri-Nejad, M; Naseri, H

2014-01-01

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection-delineation algorithms. The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method. © 2013 Elsevier Ltd. All rights reserved.

Personalized identification of differentially expressed pathways in pediatric sepsis.

PubMed

Li, Binjie; Zeng, Qiyi

2017-10-01

Sepsis is a leading killer of children worldwide with numerous differentially expressed genes reported to be associated with sepsis. Identifying core pathways in an individual is important for understanding septic mechanisms and for the future application of custom therapeutic decisions. Samples used in the study were from a control group (n=18) and pediatric sepsis group (n=52). Based on Kauffman's attractor theory, differentially expressed pathways associated with pediatric sepsis were detected as attractors. When the distribution results of attractors are consistent with the distribution of total data assessed using support vector machine, the individualized pathway aberrance score (iPAS) was calculated to distinguish differences. Through attractor and Kyoto Encyclopedia of Genes and Genomes functional analysis, 277 enriched pathways were identified as attractors. There were 81 pathways with P<0.05 and 59 pathways with P<0.01. Distribution outcomes of screened attractors were mostly consistent with the total data demonstrated by the six classifying parameters, which suggested the efficiency of attractors. Cluster analysis of pediatric sepsis using the iPAS method identified seven pathway clusters and four sample clusters. Thus, in the majority pediatric sepsis samples, core pathways can be detected as different from accumulated normal samples. In conclusion, a novel procedure that identified the dysregulated attractors in individuals with pediatric sepsis was constructed. Attractors can be markers to identify pathways involved in pediatric sepsis. iPAS may provide a correlation score for each of the signaling pathways present in an individual patient. This process may improve the personalized interpretation of disease mechanisms and may be useful in the forthcoming era of personalized medicine.

Numerical investigation of depth profiling capabilities of helium and neon ions in ion microscopy

PubMed Central

Rzeznik, Lukasz; Wirtz, Tom

2016-01-01

The analysis of polymers by secondary ion mass spectrometry (SIMS) has been a topic of interest for many years. In recent years, the primary ion species evolved from heavy monatomic ions to cluster and massive cluster primary ions in order to preserve a maximum of organic information. The progress in less-damaging sputtering goes along with a loss in lateral resolution for 2D and 3D imaging. By contrast the development of a mass spectrometer as an add-on tool for the helium ion microscope (HIM), which uses finely focussed He+ or Ne+ beams, allows for the analysis of secondary ions and small secondary cluster ions with unprecedented lateral resolution. Irradiation induced damage and depth profiling capabilities obtained with these light rare gas species have been far less investigated than ion species used classically in SIMS. In this paper we simulated the sputtering of multi-layered polymer samples using the BCA (binary collision approximation) code SD_TRIM_SP to study preferential sputtering and atomic mixing in such samples up to a fluence of 1018 ions/cm2. Results show that helium primary ions are completely inappropriate for depth profiling applications with this kind of sample materials while results for neon are similar to argon. The latter is commonly used as primary ion species in SIMS. For the two heavier species, layers separated by 10 nm can be distinguished for impact energies of a few keV. These results are encouraging for 3D imaging applications where lateral and depth information are of importance. PMID:28144525

Pathovars of Pseudomonas syringae Causing Bacterial Brown Spot and Halo Blight in Phaseolus vulgaris L. Are Distinguishable by Ribotyping

PubMed Central

González, Ana J.; Landeras, Elena; Mendoza, M. Carmen

2000-01-01

Ribotyping was evaluated as a method to differentiate between Pseudomonas syringae pv. phaseolicola and pv. syringae strains causing bacterial brown spot and halo blight diseases in Phaseolus vulgaris L. Ribotyping, with restriction enzymes BglI and SalI and using the Escherichia coli rrnB operon as the probe, differentiated 11 and 14 ribotypes, respectively, and a combination of data from both procedures yielded 19 combined ribotypes. Cluster analysis of the combined ribotypes differentiated the pathovars phaseolicola and syringae, as well as different clonal lineages within these pathovars. The potential of ribotyping to screen for correlations between lineages and factors such as geographical region and/or bean varieties is also reported. PMID:10653764

Novel probabilistic neuroclassifier

NASA Astrophysics Data System (ADS)

Hong, Jiang; Serpen, Gursel

2003-09-01

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

Nanoscale thin film growth of Au on Si(111)-7 × 7 surface by pulsed laser deposition method

NASA Astrophysics Data System (ADS)

Yokotani, Atsushi; Kameyama, Akihiro; Nakayoshi, Kohei; Matsunaga, Yuta

2017-03-01

To obtain important information for fabricating atomic-scale Au thin films that are used for biosensors, we have observed the morphology of Au particles adsorbed on a Si(111)-7 × 7 surface, which is supposed to be the initial stage of Au atomistic thin film formation. Au particles were adsorbed on the clean Si surface using a PLD method, and the adsorbed particles were observed using a scanning tunneling microscope. As the number of laser shots was increased in the PLD method, the size of the adsorbed particle became larger. The larger particles seemed to form clusters, which are aggregations of particles in which each particle is distinguished, so we call this type of cluster a film-shaped cluster. In this work, we have mainly analyzed this type of cluster. As a result the film-shaped clusters were found to have a structure of nearly monoatomic layers. The particles in the clusters were gathered closely in roughly a 3-fold structure with an inter particle distance of 0.864 nm. We propose a model for the cluster structure by modifying Au(111) face so that each observed particle consists of three Au atoms.

Design a New Strategy Based on Nanoparticle-Enhanced Chemiluminescence Sensor Array for Biothiols Discrimination

NASA Astrophysics Data System (ADS)

Shahrajabian, Maryam; Hormozi-Nezhad, M. Reza

2016-08-01

Array-based sensor is an interesting approach that suggests an alternative to expensive analytical methods. In this work, we introduce a novel, simple, and sensitive nanoparticle-based chemiluminescence (CL) sensor array for discrimination of biothiols (e.g., cysteine, glutathione and glutathione disulfide). The proposed CL sensor array is based on the CL efficiencies of four types of enhanced nanoparticle-based CL systems. The intensity of CL was altered to varying degrees upon interaction with biothiols, producing unique CL response patterns. These distinct CL response patterns were collected as “fingerprints” and were then identified through chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). The developed array was able to successfully differentiate between cysteine, glutathione and glutathione disulfide in a wide concentration range. Moreover, it was applied to distinguish among the above analytes in human plasma.

Distributed bearing fault diagnosis based on vibration analysis

NASA Astrophysics Data System (ADS)

Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

2016-01-01

Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

Magnetic signature of overbank sediment in industry impacted floodplains identified by data mining methods

NASA Astrophysics Data System (ADS)

Chudaničová, Monika; Hutchinson, Simon M.

2016-11-01

Our study attempts to identify a characteristic magnetic signature of overbank sediments exhibiting anthropogenically induced magnetic enhancement and thereby to distinguish them from unenhanced sediments with weak magnetic background values, using a novel approach based on data mining methods, thus providing a mean of rapid pollution determination. Data were obtained from 539 bulk samples from vertical profiles through overbank sediment, collected on seven rivers in the eastern Czech Republic and three rivers in northwest England. k-Means clustering and hierarchical clustering methods, paired group (UPGMA) and Ward's method, were used to divide the samples to natural groups according to their attributes. Interparametric ratios: SIRM/χ; SIRM/ARM; and S-0.1T were chosen as attributes for analyses making the resultant model more widely applicable as magnetic concentration values can differ by two orders. Division into three clusters appeared to be optimal and corresponded to inherent clusters in the data scatter. Clustering managed to separate samples with relatively weak anthropogenically induced enhancement, relatively strong anthropogenically induced enhancement and samples lacking enhancement. To describe the clusters explicitly and thus obtain a discrete magnetic signature, classification rules (JRip method) and decision trees (J4.8 and Simple Cart methods) were used. Samples lacking anthropogenic enhancement typically exhibited an S-0.1T < c. 0.5, SIRM/ARM < c. 150 and SIRM/χ < c. 6000 A m-1. Samples with magnetic enhancement all exhibited an S-0.1T > 0.5. Samples with relatively stronger anthropogenic enhancement were unequivocally distinguished from the samples with weaker enhancement by an SIRM/ARM > c. 150. Samples with SIRM/ARM in a range c. 126-150 were classified as relatively strongly enhanced when their SIRM/χ > 18 000 A m-1 and relatively less enhanced when their SIRM/χ < 18 000 A m-1. An additional rule was arbitrary added to exclude samples with χfd% > 6 per cent from anthropogenically enhanced clusters as samples with natural magnetic enhancement. The characteristics of the clusters resulted mainly from the relationship between SIRM/ARM and the S-0.1T, and SIRM/χ and the S-0.1T. Both SIRM/ARM and SIRM/χ increase with increasing S-0.1T values reflecting a greater level of anthropogenic magnetic particles. Overall, data mining methods demonstrated good potential for utilization in environmental magnetism.

Generation of high-fidelity four-photon cluster state and quantum-domain demonstration of one-way quantum computing.

PubMed

Tokunaga, Yuuki; Kuwashiro, Shin; Yamamoto, Takashi; Koashi, Masato; Imoto, Nobuyuki

2008-05-30

We experimentally demonstrate a simple scheme for generating a four-photon entangled cluster state with fidelity over 0.860+/-0.015. We show that the fidelity is high enough to guarantee that the produced state is distinguished from Greenberger-Horne-Zeilinger, W, and Dicke types of genuine four-qubit entanglement. We also demonstrate basic operations of one-way quantum computing using the produced state and show that the output state fidelities surpass classical bounds, which indicates that the entanglement in the produced state essentially contributes to the quantum operation.

Insight into the Peopling of Mainland Southeast Asia from Thai Population Genetic Structure

PubMed Central

Chaichoompu, Kridsadakorn; Ngamphiw, Chumpol; Assawamakin, Anunchai; Nuinoon, Manit; Sripichai, Orapan; Svasti, Saovaros; Fucharoen, Suthat; Praphanphoj, Verayuth; Tongsima, Sissades

2013-01-01

There is considerable ethno-linguistic and genetic variation among human populations in Asia, although tracing the origins of this diversity is complicated by migration events. Thailand is at the center of Mainland Southeast Asia (MSEA), a region within Asia that has not been extensively studied. Genetic substructure may exist in the Thai population, since waves of migration from southern China throughout its recent history may have contributed to substantial gene flow. Autosomal SNP data were collated for 438,503 markers from 992 Thai individuals. Using the available self-reported regional origin, four Thai subpopulations genetically distinct from each other and from other Asian populations were resolved by Neighbor-Joining analysis using a 41,569 marker subset. Using an independent Principal Components-based unsupervised clustering approach, four major MSEA subpopulations were resolved in which regional bias was apparent. A major ancestry component was common to these MSEA subpopulations and distinguishes them from other Asian subpopulations. On the other hand, these MSEA subpopulations were admixed with other ancestries, in particular one shared with Chinese. Subpopulation clustering using only Thai individuals and the complete marker set resolved four subpopulations, which are distributed differently across Thailand. A Sino-Thai subpopulation was concentrated in the Central region of Thailand, although this constituted a minority in an otherwise diverse region. Among the most highly differentiated markers which distinguish the Thai subpopulations, several map to regions known to affect phenotypic traits such as skin pigmentation and susceptibility to common diseases. The subpopulation patterns elucidated have important implications for evolutionary and medical genetics. The subpopulation structure within Thailand may reflect the contributions of different migrants throughout the history of MSEA. The information will also be important for genetic association studies to account for population-structure confounding effects. PMID:24223962

Characterization of diesel fuel by chemical separation combined with capillary gas chromatography (GC) isotope ratio mass spectrometry (IRMS).

PubMed

Harvey, Scott D; Jarman, Kristin H; Moran, James J; Sorensen, Christina M; Wright, Bob W

2012-09-15

The purpose of this study was to perform a preliminary investigation of compound-specific isotope analysis (CSIA) of diesel fuels to evaluate whether the technique could distinguish diesel samples from different sources/locations. The ability to differentiate or correlate diesel samples could be valuable for discovering fuel tax evasion schemes or for environmental forensic studies. Two urea adduction-based techniques were used to isolate the n-alkanes from the fuel. Both carbon isotope ratio (δ(13)C) and hydrogen isotope ratio (δD) values for the n-alkanes were then determined by CSIA in each sample. The samples investigated had δ(13)C values that ranged from -30.1‰ to -26.8‰, whereas δD values ranged from -83‰ to -156‰. Plots of δD versus δ(13)C with sample n-alkane points connected in order of increasing carbon number gave well-separated clusters with characteristic shapes for each sample. Principal components analysis (PCA) with δ(13)C, δD, or combined δ(13)C and δD data was applied to extract the maximum information content. PCA scores plots could clearly differentiate the samples, thereby demonstrating the potential of this approach for distinguishing (e.g., fingerprinting) fuel samples using δ(13)C and δD values. Copyright © 2012 Elsevier B.V. All rights reserved.

Gambling Harm and Crime Careers.

PubMed

May-Chahal, Corinne; Humphreys, Leslie; Clifton, Alison; Francis, Brian; Reith, Gerda

2017-03-01

Incarcerated populations across the world have been found to be consistently and significantly more vulnerable to problem gambling than general populations in the same countries. In an effort to gain a more specific understanding of this vulnerability the present study applied latent class analysis and criminal career theory to gambling data collected from a sample of English and Scottish, male and female prisoners (N = 1057). Theoretical links between gambling and crime were tested through three hypotheses: (1) that prisoners in the UK would have higher rates of problem gambling behaviour than the national population; (2) that if the link between gambling and crime is coincidental, gambling behaviour would be highly prevalent in an offending population, and (3) if connections between gambling behaviour and offending are co-symptomatic a mediating factor would show a strong association. The first of these was supported, the second was not supported and the third was partially supported. Latent class analysis found six gambling behaviour clusters measured by responses to the Problem Gambling Severity Index, primarily distinguished by loss chasing behaviour. Longitudinal offending data drawn from the Police National Computer database found four criminal career types, distinguished by frequency and persistence over time. A significant association was found between higher level loss chasing and high rate offending in criminal careers suggesting that impulse control may be a mediating factor for both gambling harm and criminal careers.

Cross Validity of the Behavior Style Questionnaire and Child Personality Scale in Nursery School Children.

ERIC Educational Resources Information Center

Simonds, John F.; Simonds, M. Patricia

1982-01-01

Mothers of 182 nursery school children completed the Behavior Style Questionnaire (BSQ) and the Child Personality Scale (CPS). Intercorrelational analyses showed many significantly correlated items. Scores of the five CPS factors clearly distinguished between subjects in easy and difficult BSQ clusters. Found boys significantly more introverted…

What Does Democracy Mean? Correlates of Adolescents' Views

ERIC Educational Resources Information Center

Flanagan, Constance A.; Gallay, Leslie S.; Gill, Sukhdeep; Gallay, Erin; Nti, Naana

2005-01-01

The open-ended responses of 701 7th to 12th graders to the question "What does democracy mean to you?" were analyzed. In logistic regressions, age, parental education, political discussions, and participation in extracurricular activities distinguished youth who could define democracy (53%) from those who could not. Case clustering revealed three…

Individual Differences in the Relationship between Satisfaction with Job Rewards and Job Satisfaction

ERIC Educational Resources Information Center

Hofmans, Joeri; De Gieter, Sara; Pepermans, Roland

2013-01-01

Although previous research often showed a positive relationship between pay satisfaction and job satisfaction, we dispute the universality of this finding. Cluster-wise regression analyses on three samples consistently show that two types of individuals can be distinguished, each with a different job reward-job satisfaction relationship. For the…

Evaluation of drinking quality of groundwater through multivariate techniques in urban area.

PubMed

Das, Madhumita; Kumar, A; Mohapatra, M; Muduli, S D

2010-07-01

Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.

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

Johnson, Christian I.; Caldwell, Nelson; Rich, R. Michael

We present radial velocities and chemical abundances for red giant branch stars in the Galactic bulge globular clusters NGC 6342 and NGC 6366. The velocities and abundances are based on measurements of high-resolution ( R ≳ 20,000) spectra obtained with the MMT–Hectochelle and WIYN–Hydra spectrographs. We find that NGC 6342 has a heliocentric radial velocity of +112.5 km s{sup −1} ( σ = 8.6 km s{sup −1}), NGC 6366 has a heliocentric radial velocity of −122.3 km s{sup −1} ( σ = 1.5 km s{sup −1}), and both clusters have nearly identical metallicities ([Fe/H] ≈ −0.55). NGC 6366 shows evidencemore » of a moderately extended O–Na anti-correlation, but more data are needed for NGC 6342 to determine if this cluster also exhibits the typical O–Na relation likely found in all other Galactic globular clusters. The two clusters are distinguished from similar metallicity field stars as having larger [Na/Fe] spreads and enhanced [La/Fe] ratios, but we find that NGC 6342 and NGC 6366 display α and Fe-peak element abundance patterns that are typical of other metal-rich ([Fe/H] > −1) inner Galaxy clusters. However, the median [La/Fe] abundance may vary from cluster-to-cluster.« less

Time-course microarray analysis for identifying candidate genes involved in obesity-associated pathological changes in the mouse colon.

PubMed

Bae, Yun Jung; Kim, Sung-Eun; Hong, Seong Yeon; Park, Taesun; Lee, Sang Gyu; Choi, Myung-Sook; Sung, Mi-Kyung

2016-01-01

Obesity is known to increase the risk of colorectal cancer. However, mechanisms underlying the pathogenesis of obesity-induced colorectal cancer are not completely understood. The purposes of this study were to identify differentially expressed genes in the colon of mice with diet-induced obesity and to select candidate genes as early markers of obesity-associated abnormal cell growth in the colon. C57BL/6N mice were fed normal diet (11% fat energy) or high-fat diet (40% fat energy) and were euthanized at different time points. Genome-wide expression profiles of the colon were determined at 2, 4, 8, and 12 weeks. Cluster analysis was performed using expression data of genes showing log 2 fold change of ≥1 or ≤-1 (twofold change), based on time-dependent expression patterns, followed by virtual network analysis. High-fat diet-fed mice showed significant increase in body weight and total visceral fat weight over 12 weeks. Time-course microarray analysis showed that 50, 47, 36, and 411 genes were differentially expressed at 2, 4, 8, and 12 weeks, respectively. Ten cluster profiles representing distinguishable patterns of genes differentially expressed over time were determined. Cluster 4, which consisted of genes showing the most significant alterations in expression in response to high-fat diet over 12 weeks, included Apoa4 (apolipoprotein A-IV), Ppap2b (phosphatidic acid phosphatase type 2B), Cel (carboxyl ester lipase), and Clps (colipase, pancreatic), which interacted strongly with surrounding genes associated with colorectal cancer or obesity. Our data indicate that Apoa4 , Ppap2b , Cel , and Clps are candidate early marker genes associated with obesity-related pathological changes in the colon. Genome-wide analyses performed in the present study provide new insights on selecting novel genes that may be associated with the development of diseases of the colon.

Whole genome sequencing of Brucella melitensis isolated from 57 patients in Germany reveals high diversity in strains from Middle East.

PubMed

Georgi, Enrico; Walter, Mathias C; Pfalzgraf, Marie-Theres; Northoff, Bernd H; Holdt, Lesca M; Scholz, Holger C; Zoeller, Lothar; Zange, Sabine; Antwerpen, Markus H

2017-01-01

Brucellosis, a worldwide common bacterial zoonotic disease, has become quite rare in Northern and Western Europe. However, since 2014 a significant increase of imported infections caused by Brucella (B.) melitensis has been noticed in Germany. Patients predominantly originated from Middle East including Turkey and Syria. These circumstances afforded an opportunity to gain insights into the population structure of Brucella strains. Brucella-isolates from 57 patients were recovered between January 2014 and June 2016 with culture confirmed brucellosis by the National Consultant Laboratory for Brucella. Their whole genome sequences were generated using the Illumina MiSeq platform. A whole genome-based SNP typing assay was developed in order to resolve geographically attributed genetic clusters. Results were compared to MLVA typing results, the current gold-standard of Brucella typing. In addition, sequences were examined for possible genetic variation within target regions of molecular diagnostic assays. Phylogenetic analyses revealed spatial clustering and distinguished strains from different patients in either case, whereas multiple isolates from a single patient or technical replicates showed identical SNP and MLVA profiles. By including WGS data from the NCBI database, five major genotypes were identified. Notably, strains originating from Turkey showed a high diversity and grouped into seven subclusters of genotype II. MLVA analysis congruently clustered all isolates and predominantly matched the East Mediterranean genetic clade. This study confirms whole-genome based SNP-analysis as a powerful tool for accurate typing of B. melitensis. Furthermore it allows special allocation and therefore provides useful information on the geographic origin for trace-back analysis. However, the lack of reliable metadata in public databases often prevents a resolution below geographic regions or country levels and corresponding precise trace-back analysis. Once this obstacle is resolved, WGS-derived bacterial typing adds an important method to complement epidemiological surveys during outbreak investigations. This is the first report of a detailed genetic investigation of an extensive collection of B. melitensis strains isolated from human cases in Germany.

Studying the Therapeutic Process by Observing Clinicians' In-Session Behaviour.

PubMed

Montaño-Fidalgo, Montserrat; Ruiz, Elena M; Calero-Elvira, Ana; Froján-Parga, María Xesús

2015-01-01

This paper presents a further step in the use and validation of a systematic, functional-analytic method of describing psychologists' verbal behaviour during therapy. We observed recordings from 92 clinical sessions of 19 adults (14 women and 5 men of Caucasian origin, with ages ranging from 19 to 51 years) treated by nine cognitive-behavioural therapists (eight women and one man, Caucasian as well, with ages ranging from 25 to 48 years). The therapists' verbal behaviour was codified and then classified according to its possible functionality. A cluster analysis of the data, followed by a discriminant analysis, showed that the therapists' verbal behaviour tended to aggregate around four types of session differentiated by their clinical objective (assessment, explanation, treatment and consolidation). These results confirm the validity of our method and enable us to further describe clinical phenomena by distinguishing psychologists' classes of clinically relevant activities. Specific learning mechanisms may be responsible for clinical change within each class. These issues should be analysed more closely when explaining therapeutic phenomena and when developing more effective forms of clinical intervention. We described therapists' verbal behaviour in a focused fashion so as to develop new research methods that evaluate psychological work moment by moment. We performed a cluster analysis in order to evaluate how the therapists' verbal behaviour was distributed throughout the intervention. A discriminant analysis gave us further information about the statistical significance and possible nature of the clusters we observed. The therapists' verbal behaviour depended on current clinical objectives and could be classified into four classes of clinically relevant activities: evaluation, explanation, treatment and consolidation. Some of the therapist's verbalizations were more important than others when carrying out these clinically relevant activities. The distribution of the therapists' verbal behaviour across classes may provide us with clues regarding the functionality of their in-session verbal behaviour. Copyright © 2014 John Wiley & Sons, Ltd.

Molecular signatures of fossil leaves provide unexpected new evidence for extinct plant relationships.

PubMed

Vajda, Vivi; Pucetaite, Milda; McLoughlin, Stephen; Engdahl, Anders; Heimdal, Jimmy; Uvdal, Per

2017-08-01

Gene sequences form the primary basis for understanding the relationships among extant plant groups, but genetic data are unavailable from fossils to evaluate the affinities of extinct taxa. Here we show that geothermally resistant fossil cuticles of seed-bearing plants, analysed with Fourier transform infrared (FTIR) spectroscopy and hierarchical cluster analysis (HCA), retain biomolecular suites that consistently distinguish major taxa even after experiencing different diagenetic histories. Our results reveal that similarities between the cuticular biochemical signatures of major plant groups (extant and fossil) are mostly consistent with recent phylogenetic hypotheses based on molecular and morphological data. Our novel chemotaxonomic data also support the hypothesis that the extinct Nilssoniales and Bennettitales are closely allied, but only distantly related to Cycadales. The chemical signature of the cuticle of Czekanowskia (Leptostrobales) is strongly similar to that of Ginkgo leaves and supports a close evolutionary relationship between these groups. Finally, our results also reveal that the extinct putative araucariacean, Allocladus, when analysed through HCA, is grouped closer to Ginkgoales than to conifers. Thus, in the absence of modern relatives yielding molecular information, FTIR spectroscopy provides valuable proxy biochemical data complementing morphological characters to distinguish fossil taxa and to help elucidate extinct plant relationships.

Optical mapping of prefrontal brain connectivity and activation during emotion anticipation.

PubMed

Wang, Meng-Yun; Lu, Feng-Mei; Hu, Zhishan; Zhang, Juan; Yuan, Zhen

2018-09-17

Accumulated neuroimaging evidence shows that the dorsal lateral prefrontal cortex (dlPFC) is activated during emotion anticipation. The aim of this work is to examine the brain connectivity and activation differences in dlPFC between the positive, neutral and negative emotion anticipation by using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses were first assessed for all subjects during the performance of various emotion anticipation tasks. And then small-world analysis was performed, in which the small-world network indicators including the clustering coefficient, average path length, average node degree, and measure of small-world index were calculated for the functional brain networks associated with the positive, neutral and negative emotion anticipation, respectively. We discovered that compared to negative and neutral emotion anticipation, the positive one exhibited enhanced brain activation in the left dlPFC. Although the functional brain networks for the three emotion anticipation cases manifested the small-world properties regarding the clustering coefficient, average path length, average node degree, and measure of small-world index, the positive one showed significantly higher clustering coefficient and shorter average path length than those from the neutral and negative cases. Consequently, the small-world network indicators and brain activation in dlPPC were able to distinguish well between the positive, neutral and negative emotion anticipation. Copyright © 2018 Elsevier B.V. All rights reserved.

The utility of multiple molecular methods including whole genome sequencing as tools to differentiate Escherichia coli O157:H7 outbreaks.

PubMed

Berenger, Byron M; Berry, Chrystal; Peterson, Trevor; Fach, Patrick; Delannoy, Sabine; Li, Vincent; Tschetter, Lorelee; Nadon, Celine; Honish, Lance; Louie, Marie; Chui, Linda

2015-01-01

A standardised method for determining Escherichia coli O157:H7 strain relatedness using whole genome sequencing or virulence gene profiling is not yet established. We sought to assess the capacity of either high-throughput polymerase chain reaction (PCR) of 49 virulence genes, core-genome single nt variants (SNVs) or k-mer clustering to discriminate between outbreak-associated and sporadic E. coli O157:H7 isolates. Three outbreaks and multiple sporadic isolates from the province of Alberta, Canada were included in the study. Two of the outbreaks occurred concurrently in 2014 and one occurred in 2012. Pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem repeat analysis (MLVA) were employed as comparator typing methods. The virulence gene profiles of isolates from the 2012 and 2014 Alberta outbreak events and contemporary sporadic isolates were mostly identical; therefore the set of virulence genes chosen in this study were not discriminatory enough to distinguish between outbreak clusters. Concordant with PFGE and MLVA results, core genome SNV and k-mer phylogenies clustered isolates from the 2012 and 2014 outbreaks as distinct events. k-mer phylogenies demonstrated increased discriminatory power compared with core SNV phylogenies. Prior to the widespread implementation of whole genome sequencing for routine public health use, issues surrounding cost, technical expertise, software standardisation, and data sharing/comparisons must be addressed.

Kinematic Clues to OB Field Star Origins: Radial Velocities, Runaways, and Binaries

NASA Astrophysics Data System (ADS)

Januszewski, Helen; Castro, Norberto; Oey, Sally; Becker, Juliette; Kratter, Kaitlin M.; Mateo, Mario; Simón-Díaz, Sergio; Bjorkman, Jon E.; Bjorkman, Karen; Sigut, Aaron; Smullen, Rachel; M2FS Team

2018-01-01

Field OB stars are a crucial probe of star formation in extreme conditions. Properties of massive stars formed in relative isolation can distinguish between competing star formation theories, while the statistics of runaway stars allow an indirect test of the densest conditions in clusters. To address these questions, we have obtained multi-epoch, spectroscopic observations for a spatially complete sample of 48 OB field stars in the SMC Wing with the IMACS and M2FS multi-object spectrographs at the Magellan Telescopes. The observations span 3-6 epochs per star, with sampling frequency ranging from one day to about one year. From these spectra, we have calculated the radial velocities (RVs) and, in particular, the systemic velocities for binaries. Thus, we present the intrinsic RV distribution largely uncontaminated by binary motions. We estimate the runaway frequency, corresponding to the high velocity stars in our sample, and we also constrain the binary frequency. The binary frequency and fitted orbital parameters also place important constraints on star formation theories, as these properties drive the process of runaway ejection in clusters, and we discuss these properties as derived from our sample. This unique kinematic analysis of a high mass field star population thus provides a new look at the processes governing formation and interaction of stars in environments at extreme densities, from isolation to dense clusters.

Cognitive subtypes of mathematics learning difficulties in primary education.

PubMed

Bartelet, Dimona; Ansari, Daniel; Vaessen, Anniek; Blomert, Leo

2014-03-01

It has been asserted that children with mathematics learning difficulties (MLD) constitute a heterogeneous group. To date, most researchers have investigated differences between predefined MLD subtypes. Specifically MLD children are frequently categorized a priori into groups based on the presence or absence of an additional disorder, such as a reading disorder, to examine cognitive differences between MLD subtypes. In the current study 226 third to six grade children (M age=131 months) with MLD completed a selection of number specific and general cognitive measures. The data driven approach was used to identify the extent to which performance of the MLD children on these measures could be clustered into distinct groups. In particular, after conducting a factor analysis, a 200 times repeated K-means clustering approach was used to classify the children's performance. Results revealed six distinguishable clusters of MLD children, specifically (a) a weak mental number line group, (b) weak ANS group, (c) spatial difficulties group, (d) access deficit group, (e) no numerical cognitive deficit group and (f) a garden-variety group. These findings imply that different cognitive subtypes of MLD exist and that these can be derived from data-driven approaches to classification. These findings strengthen the notion that MLD is a heterogeneous disorder, which has implications for the way in which intervention may be tailored for individuals within the different subtypes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Phylogenomic Study of Burkholderia glathei-like Organisms, Proposal of 13 Novel Burkholderia Species and Emended Descriptions of Burkholderia sordidicola, Burkholderia zhejiangensis, and Burkholderia grimmiae

PubMed Central

Peeters, Charlotte; Meier-Kolthoff, Jan P.; Verheyde, Bart; De Brandt, Evie; Cooper, Vaughn S.; Vandamme, Peter

2016-01-01

Partial gyrB gene sequence analysis of 17 isolates from human and environmental sources revealed 13 clusters of strains and identified them as Burkholderia glathei clade (BGC) bacteria. The taxonomic status of these clusters was examined by whole-genome sequence analysis, determination of the G+C content, whole-cell fatty acid analysis and biochemical characterization. The whole-genome sequence-based phylogeny was assessed using the Genome Blast Distance Phylogeny (GBDP) method and an extended multilocus sequence analysis (MLSA) approach. The results demonstrated that these 17 BGC isolates represented 13 novel Burkholderia species that could be distinguished by both genotypic and phenotypic characteristics. BGC strains exhibited a broad metabolic versatility and developed beneficial, symbiotic, and pathogenic interactions with different hosts. Our data also confirmed that there is no phylogenetic subdivision in the genus Burkholderia that distinguishes beneficial from pathogenic strains. We therefore propose to formally classify the 13 novel BGC Burkholderia species as Burkholderia arvi sp. nov. (type strain LMG 29317T = CCUG 68412T), Burkholderia hypogeia sp. nov. (type strain LMG 29322T = CCUG 68407T), Burkholderia ptereochthonis sp. nov. (type strain LMG 29326T = CCUG 68403T), Burkholderia glebae sp. nov. (type strain LMG 29325T = CCUG 68404T), Burkholderia pedi sp. nov. (type strain LMG 29323T = CCUG 68406T), Burkholderia arationis sp. nov. (type strain LMG 29324T = CCUG 68405T), Burkholderia fortuita sp. nov. (type strain LMG 29320T = CCUG 68409T), Burkholderia temeraria sp. nov. (type strain LMG 29319T = CCUG 68410T), Burkholderia calidae sp. nov. (type strain LMG 29321T = CCUG 68408T), Burkholderia concitans sp. nov. (type strain LMG 29315T = CCUG 68414T), Burkholderia turbans sp. nov. (type strain LMG 29316T = CCUG 68413T), Burkholderia catudaia sp. nov. (type strain LMG 29318T = CCUG 68411T) and Burkholderia peredens sp. nov. (type strain LMG 29314T = CCUG 68415T). Furthermore, we present emended descriptions of the species Burkholderia sordidicola, Burkholderia zhejiangensis and Burkholderia grimmiae. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA and gyrB gene sequences determined in this study are LT158612-LT158624 and LT158625-LT158641, respectively. PMID:27375597

Two-step evolution of endosymbiosis between hydra and algae.

PubMed

Ishikawa, Masakazu; Shimizu, Hiroshi; Nozawa, Masafumi; Ikeo, Kazuho; Gojobori, Takashi

2016-10-01

In the Hydra vulgaris group, only 2 of the 25 strains in the collection of the National Institute of Genetics in Japan currently show endosymbiosis with green algae. However, whether the other non-symbiotic strains also have the potential to harbor algae remains unknown. The endosymbiotic potential of non-symbiotic strains that can harbor algae may have been acquired before or during divergence of the strains. With the aim of understanding the evolutionary process of endosymbiosis in the H. vulgaris group, we examined the endosymbiotic potential of non-symbiotic strains of the H. vulgaris group by artificially introducing endosymbiotic algae. We found that 12 of the 23 non-symbiotic strains were able to harbor the algae until reaching the grand-offspring through the asexual reproduction by budding. Moreover, a phylogenetic analysis of mitochondrial genome sequences showed that all the strains with endosymbiotic potential grouped into a single cluster (cluster γ). This cluster contained two strains (J7 and J10) that currently harbor algae; however, these strains were not the closest relatives. These results suggest that evolution of endosymbiosis occurred in two steps; first, endosymbiotic potential was gained once in the ancestor of the cluster γ lineage; second, strains J7 and J10 obtained algae independently after the divergence of the strains. By demonstrating the evolution of the endosymbiotic potential in non-symbiotic H. vulgaris group strains, we have clearly distinguished two evolutionary steps. The step-by-step evolutionary process provides significant insight into the evolution of endosymbiosis in cnidarians. Copyright © 2016 Elsevier Inc. All rights reserved.

Tumor-initiating cells of breast and prostate origin show alterations in the expression of genes related to iron metabolism

PubMed Central

Tomkova, Veronika; Korenkova, Vlasta; Langerova, Lucie; Simonova, Ekaterina; Zjablovskaja, Polina; Alberich-Jorda, Meritxell; Neuzil, Jiri; Truksa, Jaroslav

2017-01-01

The importance of iron in the growth and progression of tumors has been widely documented. In this report, we show that tumor-initiating cells (TICs), represented by spheres derived from the MCF7 cell line, exhibit higher intracellular labile iron pool, mitochondrial iron accumulation and are more susceptible to iron chelation. TICs also show activation of the IRP/IRE system, leading to higher iron uptake and decrease in iron storage, suggesting that level of properly assembled cytosolic iron-sulfur clusters (FeS) is reduced. This finding is confirmed by lower enzymatic activity of aconitase and FeS cluster biogenesis enzymes, as well as lower levels of reduced glutathione, implying reduced FeS clusters synthesis/utilization in TICs. Importantly, we have identified specific gene signature related to iron metabolism consisting of genes regulating iron uptake, mitochondrial FeS cluster biogenesis and hypoxic response (ABCB10, ACO1, CYBRD1, EPAS1, GLRX5, HEPH, HFE, IREB2, QSOX1 and TFRC). Principal component analysis based on this signature is able to distinguish TICs from cancer cells in vitro and also Leukemia-initiating cells (LICs) from non-LICs in the mouse model of acute promyelocytic leukemia (APL). Majority of the described changes were also recapitulated in an alternative model represented by MCF7 cells resistant to tamoxifen (TAMR) that exhibit features of TICs. Our findings point to the critical importance of redox balance and iron metabolism-related genes and proteins in the context of cancer and TICs that could be potentially used for cancer diagnostics or therapy. PMID:28031527

Weak Lensing Peaks in Simulated Light-Cones: Investigating the Coupling between Dark Matter and Dark Energy

NASA Astrophysics Data System (ADS)

Giocoli, Carlo; Moscardini, Lauro; Baldi, Marco; Meneghetti, Massimo; Metcalf, Robert B.

2018-05-01

In this paper, we study the statistical properties of weak lensing peaks in light-cones generated from cosmological simulations. In order to assess the prospects of such observable as a cosmological probe, we consider simulations that include interacting Dark Energy (hereafter DE) models with coupling term between DE and Dark Matter. Cosmological models that produce a larger population of massive clusters have more numerous high signal-to-noise peaks; among models with comparable numbers of clusters those with more concentrated haloes produce more peaks. The most extreme model under investigation shows a difference in peak counts of about 20% with respect to the reference ΛCDM model. We find that peak statistics can be used to distinguish a coupling DE model from a reference one with the same power spectrum normalisation. The differences in the expansion history and the growth rate of structure formation are reflected in their halo counts, non-linear scale features and, through them, in the properties of the lensing peaks. For a source redshift distribution consistent with the expectations of future space-based wide field surveys, we find that typically seventy percent of the cluster population contributes to weak-lensing peaks with signal-to-noise ratios larger than two, and that the fraction of clusters in peaks approaches one-hundred percent for haloes with redshift z ≤ 0.5. Our analysis demonstrates that peak statistics are an important tool for disentangling DE models by accurately tracing the structure formation processes as a function of the cosmic time.

Transport in the Subtropical Lowermost Stratosphere during CRYSTAL-FACE

NASA Technical Reports Server (NTRS)

Pittman, Jasna V.; Weinstock, elliot M.; Oglesby, Robert J.; Sayres, David S.; Smith, Jessica B.; Anderson, James G.; Cooper, Owen R.; Wofsy, Steven C.; Xueref, Irene; Gerbig, Cristoph;

Transport in the Subtropical Lowermost Stratosphere during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment

NASA Technical Reports Server (NTRS)

Pittman, Jasna V.; Weinstock, Elliot M.; Oglesby, Robert J.; Sayres, David S.; Smith, Jessica B.; Anderson, James G.; Cooper, Owen R.; Wofsy, Steven C.; Xueref, Irene; Gerbig, Cristoph;

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

Neurocognitive disorders: cluster 1 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Sachdev, P; Andrews, G; Hobbs, M J; Sunderland, M; Anderson, T M

2009-12-01

In an effort to group mental disorders on the basis of aetiology, five clusters have been proposed. In this paper, we consider the validity of the first cluster, neurocognitive disorders, within this proposal. These disorders are categorized as 'Dementia, Delirium, and Amnestic and Other Cognitive Disorders' in DSM-IV and 'Organic, including Symptomatic Mental Disorders' in ICD-10. We reviewed the literature in relation to 11 validating criteria proposed by a Study Group of the DSM-V Task Force as applied to the cluster of neurocognitive disorders. 'Neurocognitive' replaces the previous terms 'cognitive' and 'organic' used in DSM-IV and ICD-10 respectively as the descriptor for disorders in this cluster. Although cognitive/organic problems are present in other disorders, this cluster distinguishes itself by the demonstrable neural substrate abnormalities and the salience of cognitive symptoms and deficits. Shared biomarkers, co-morbidity and course offer less persuasive evidence for a valid cluster of neurocognitive disorders. The occurrence of these disorders subsequent to normal brain development sets this cluster apart from neurodevelopmental disorders. The aetiology of the disorders is varied, but the neurobiological underpinnings are better understood than for mental disorders in any other cluster. Neurocognitive disorders meet some of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster. Further developments in the aetiopathogenesis of these disorders will enhance the clinical utility of this cluster.

Whole-Genome Sequencing Analysis of Salmonella enterica Serovar Enteritidis Isolates in Chile Provides Insights into Possible Transmission between Gulls, Poultry, and Humans.

PubMed

Toro, Magaly; Retamal, Patricio; Ayers, Sherry; Barreto, Marlen; Allard, Marc; Brown, Eric W; Gonzalez-Escalona, Narjol

2016-10-15

Salmonella enterica subsp. enterica serotype Enteritidis is a major cause of human salmonellosis worldwide; however, little is known about the genetic relationships between S Enteritidis clinical strains and S Enteritidis strains from other sources in Chile. We compared the whole genomes of 30 S Enteritidis strains isolated from gulls, domestic chicken eggs, and humans in Chile, to investigate their phylogenetic relationships and to establish their relatedness to international strains. Core genome multilocus sequence typing (cgMLST) analysis showed that only 246/4,065 shared loci differed among these Chilean strains, separating them into two clusters (I and II), with cluster II being further divided into five subclusters. One subcluster (subcluster 2) contained strains from all surveyed sources that differed at 1 to 18 loci (of 4,065 loci) with 1 to 18 single-nucleotide polymorphisms (SNPs), suggesting interspecies transmission of S Enteritidis in Chile. Moreover, clusters were formed by strains that were distant geographically, which could imply that gulls might be spreading the pathogen throughout the country. Our cgMLST analysis, using other S Enteritidis genomes available in the National Center for Biotechnology Information (NCBI) database, showed that S Enteritidis strains from Chile and the United States belonged to different lineages, which suggests that S Enteritidis regional markers might exist and could be used for trace-back investigations. This study highlights the importance of gulls in the spread of Salmonella Enteritidis in Chile. We revealed a close genetic relationship between some human and gull S Enteritidis strains (with as few as 2 of 4,065 genes being different), and we also found that gull strains were present in clusters formed by strains isolated from other sources or distant locations. Together with previously published evidence, this suggests that gulls might be spreading this pathogen between different regions in Chile and that some of those strains have been transmitted to humans. Moreover, we discovered that Chilean S Enteritidis strains clustered separately from most of S Enteritidis strains isolated throughout the world (in the GenBank database) and thus it might be possible to distinguish the geographical origins of strains based on specific genomic features. This could be useful for trace-back investigations of foodborne illnesses throughout the world. Copyright © 2016 Toro et al.

Whole-Genome Sequencing Analysis of Salmonella enterica Serovar Enteritidis Isolates in Chile Provides Insights into Possible Transmission between Gulls, Poultry, and Humans

PubMed Central

Ayers, Sherry; Barreto, Marlen; Allard, Marc; Brown, Eric W.

2016-01-01

ABSTRACT Salmonella enterica subsp. enterica serotype Enteritidis is a major cause of human salmonellosis worldwide; however, little is known about the genetic relationships between S. Enteritidis clinical strains and S. Enteritidis strains from other sources in Chile. We compared the whole genomes of 30 S. Enteritidis strains isolated from gulls, domestic chicken eggs, and humans in Chile, to investigate their phylogenetic relationships and to establish their relatedness to international strains. Core genome multilocus sequence typing (cgMLST) analysis showed that only 246/4,065 shared loci differed among these Chilean strains, separating them into two clusters (I and II), with cluster II being further divided into five subclusters. One subcluster (subcluster 2) contained strains from all surveyed sources that differed at 1 to 18 loci (of 4,065 loci) with 1 to 18 single-nucleotide polymorphisms (SNPs), suggesting interspecies transmission of S. Enteritidis in Chile. Moreover, clusters were formed by strains that were distant geographically, which could imply that gulls might be spreading the pathogen throughout the country. Our cgMLST analysis, using other S. Enteritidis genomes available in the National Center for Biotechnology Information (NCBI) database, showed that S. Enteritidis strains from Chile and the United States belonged to different lineages, which suggests that S. Enteritidis regional markers might exist and could be used for trace-back investigations. IMPORTANCE This study highlights the importance of gulls in the spread of Salmonella Enteritidis in Chile. We revealed a close genetic relationship between some human and gull S. Enteritidis strains (with as few as 2 of 4,065 genes being different), and we also found that gull strains were present in clusters formed by strains isolated from other sources or distant locations. Together with previously published evidence, this suggests that gulls might be spreading this pathogen between different regions in Chile and that some of those strains have been transmitted to humans. Moreover, we discovered that Chilean S. Enteritidis strains clustered separately from most of S. Enteritidis strains isolated throughout the world (in the GenBank database) and thus it might be possible to distinguish the geographical origins of strains based on specific genomic features. This could be useful for trace-back investigations of foodborne illnesses throughout the world. PMID:27520817

Strengths and limitations of molecular subtyping in a community outbreak of Legionnaires' disease.

PubMed

Kool, J L; Buchholz, U; Peterson, C; Brown, E W; Benson, R F; Pruckler, J M; Fields, B S; Sturgeon, J; Lehnkering, E; Cordova, R; Mascola, L M; Butler, J C

2000-12-01

An epidemiological and microbiological investigation of a cluster of eight cases of Legionnaires' disease in Los Angeles County in November 1997 yielded conflicting results. The epidemiological part of the investigation implicated one of several mobile cooling towers used by a film studio in the centre of the outbreak area. However, water sampled from these cooling towers contained L. pneumophila serogroup 1 of another subtype than the strain that was recovered from case-patients in the outbreak. Samples from two cooling towers located downwind from all of the case-patients contained a Legionella strain that was indistinguishable from the outbreak strain by four subtyping techniques (AP-PCR, PFGE, MAb, and MLEE). It is unlikely that these cooling towers were the source of infection for all the case-patients, and they were not associated with risk of disease in the case-control study. The outbreak strain also was not distinguishable, by three subtyping techniques (AP-PCR, PFGE, and MAb), from a L. pneumophila strain that had caused an outbreak in Providence, RI, in 1993. Laboratory cross-contamination was unlikely because the initial subtyping was done in different laboratories. In this investigation, microbiology was helpful for distinguishing the outbreak cluster from unrelated cases of Legionnaires' disease occurring elsewhere. However, multiple subtyping techniques failed to distinguish environmental sources that were probably not associated with the outbreak. Persons investigating Legionnaires' disease outbreaks should be aware that microbiological subtyping does not always identify a source with absolute certainty.

A new method to evaluate the unfolding activity of chaperone unit ClpA based on Fe-S cluster disruption.

PubMed

Ohgita, Takashi; Okuno, Takashi; Hama, Susumu; Tsuchiya, Hiroyuki; Kogure, Kentaro

2011-01-01

ATP-dependent proteases unfold their substrates and then refold (via chaperone activity) or degrade (via protease activity) them. The proteases choose between these two activities by selecting their substrates; however, little is known about their substrate selection mechanism. The present study attempts to clarify this mechanism by investigating the role of the Escherichia coli (E. coli) ATP-dependent protease ClpAP. To address this, a reaction system that can measure both chaperone and protease activities simultaneously must be constructed. However, the chaperone activities cannot be evaluated in the presence of protease units. Green fluorescent protein (GFP) is usually used as a model substrate of ClpAP; the fluorescence decrease reflects the degradation of substrates. However, it is difficult to evaluate the chaperone activity of ClpAP using this system, because it cannot distinguish between intact and refolded substrates. Therefore, it is necessary to evaluate the exact unfolding activity while avoiding restoration of substrate spectroscopic characteristics due to chaperone activity. In this study, E. coli Ferredoxin (Fd) was used as a new model substrate for ClpAP to evaluate its unfolding activity. Intact and refolded substrates may be distinguished by the existence of an Fd Fe-S cluster. To verify this hypothesis, the absorption spectrum of Fd complexed with ClpA, the chaperone unit of ClpAP, was measured. A decrease in two peaks derived from the Fe-S cluster was observed, indicating that the Fe-S cluster of Fd was disrupted by the ClpA chaperone. This reaction system should prove useful to evaluate the exact unfolding activity of ATP-dependent proteases.

An integrated view of the role of miR-130b/301b miRNA cluster in prostate cancer.

PubMed

Fort, Rafael Sebastián; Mathó, Cecilia; Oliveira-Rizzo, Carolina; Garat, Beatriz; Sotelo-Silveira, José Roberto; Duhagon, María Ana

2018-01-01

Prostate cancer is a major health problem worldwide due to its high incidence morbidity and mortality. There is currently a need of improved biomarkers, capable to distinguish mild versus aggressive forms of the disease, and thus guide therapeutic decisions. Although miRNAs deregulated in cancer represent exciting candidates as biomarkers, its scientific literature is frequently fragmented in dispersed studies. This problem is aggravated for miRNAs belonging to miRNA gene clusters with shared target genes. The miRNA cluster composed by hsa-mir-130b and hsa-mir-301b precursors was recently involved in prostate cancer pathogenesis, yet different studies assigned it opposite effects on the disease. We sought to elucidate the role of the human miR-130b/301b miRNA cluster in prostate cancer through a comprehensive data analysis of most published clinical cohorts. We interrogated methylomes, transcriptomes and patient clinical data, unifying previous reports and adding original analysis using the largest available cohort (TCGA-PRAD). We found that hsa-miR-130b-3p and hsa-miR-301b-3p are upregulated in neoplastic vs normal prostate tissue, as well as in metastatic vs primary sites. However, this increase in expression is not due to a decrease of the global DNA methylation of the genes in prostate tissues, as the promoter of the gene remains lowly methylated in normal and neoplastic tissue. A comparison of the levels of human miR-130b/301b and all the clinical variables reported for the major available cohorts, yielded positive correlations with malignance, specifically significant for T-stage, residual tumor status and primary therapy outcome. The assessment of the correlations between the hsa-miR-130b-3p and hsa-miR-301b-3p and candidate target genes in clinical samples, supports their repression of tumor suppressor genes in prostate cancer. Altogether, these results favor an oncogenic role of miR-130b/301b cluster in prostate cancer.

Molecular evidence of Burkholderia pseudomallei genotypes based on geographical distribution.

PubMed

Zulkefli, Noorfatin Jihan; Mariappan, Vanitha; Vellasamy, Kumutha Malar; Chong, Chun Wie; Thong, Kwai Lin; Ponnampalavanar, Sasheela; Vadivelu, Jamuna; Teh, Cindy Shuan Ju

2016-01-01

Background. Central intermediary metabolism (CIM) in bacteria is defined as a set of metabolic biochemical reactions within a cell, which is essential for the cell to survive in response to environmental perturbations. The genes associated with CIM are commonly found in both pathogenic and non-pathogenic strains. As these genes are involved in vital metabolic processes of bacteria, we explored the efficiency of the genes in genotypic characterization of Burkholderia pseudomallei isolates, compared with the established pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) schemes. Methods. Nine previously sequenced B. pseudomallei isolates from Malaysia were characterized by PFGE, MLST and CIM genes. The isolates were later compared to the other 39 B. pseudomallei strains, retrieved from GenBank using both MLST and sequence analysis of CIM genes. UniFrac and hierachical clustering analyses were performed using the results generated by both MLST and sequence analysis of CIM genes. Results. Genetic relatedness of nine Malaysian B. pseudomallei isolates and the other 39 strains was investigated. The nine Malaysian isolates were subtyped into six PFGE profiles, four MLST profiles and five sequence types based on CIM genes alignment. All methods demonstrated the clonality of OB and CB as well as CMS and THE. However, PFGE showed less than 70% similarity between a pair of morphology variants, OS and OB. In contrast, OS was identical to the soil isolate, MARAN. To have a better understanding of the genetic diversity of B. pseudomallei worldwide, we further aligned the sequences of genes used in MLST and genes associated with CIM for the nine Malaysian isolates and 39 B. pseudomallei strains from NCBI database. Overall, based on the CIM genes, the strains were subtyped into 33 profiles where majority of the strains from Asian countries were clustered together. On the other hand, MLST resolved the isolates into 31 profiles which formed three clusters. Hierarchical clustering using UniFrac distance suggested that the isolates from Australia were genetically distinct from the Asian isolates. Nevertheless, statistical significant differences were detected between isolates from Malaysia, Thailand and Australia. Discussion. Overall, PFGE showed higher discriminative power in clustering the nine Malaysian B. pseudomallei isolates and indicated its suitability for localized epidemiological study. Compared to MLST, CIM genes showed higher resolution in distinguishing those non-related strains and better clustering of strains from different geographical regions. A closer genetic relatedness of Malaysian isolates with all Asian strains in comparison to Australian strains was observed. This finding was supported by UniFrac analysis which resulted in geographical segregation between Australia and the Asian countries.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Adansonian Analysis and Deoxyribonucleic Acid Base Composition of Serratia marcescens

PubMed Central

Colwell, R. R.; Mandel, M.

1965-01-01

Colwell, R. R. (Georgetown University, Washington, D.C.), and M. Mandel. Adansonian analysis and deoxyribonucleic acid base composition of Serratia marcescens. J. Bacteriol. 89:454–461. 1965.—A total of 33 strains of Serratia marcescens were subjected to Adansonian analysis for which more than 200 coded features for each of the organisms were included. In addition, the base composition [expressed as moles per cent guanine + cytosine (G + C)] of the deoxyribonucleic acid (DNA) prepared from each of the strains was determined. Except for four strains which were intermediate between Serratia and the Hafnia and Aerobacter group C of Edwards and Ewing, the S. marcescens species group proved to be extremely homogeneous, and the different strains showed high affinities for each other (mean similarity, ¯S = 77%). The G + C ratio of the DNA from the Serratia strains ranged from 56.2 to 58.4% G + C. Many species names have been listed for the genus, but only a single clustering of the strains was obtained at the species level, for which the species name S. marcescens was retained. S. kiliensis, S. indica, S. plymuthica, and S. marinorubra could not be distinguished from S. marcescens; it was concluded, therefore, that there is only a single species in the genus. The variety designation kiliensis does not appear to be valid, since no subspecies clustering of strains with negative Voges-Proskauer reactions could be detected. The characteristics of the species are listed, and a description of S. marcescens is presented. PMID:14255714

Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers.

PubMed

Carlson, Alyssa K; Rawle, Rachel A; Adams, Erik; Greenwood, Mark C; Bothner, Brian; June, Ronald K

2018-05-05

Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease. Copyright © 2018 Elsevier Inc. All rights reserved.

Identification and classification of cathinone unknowns by statistical analysis processing of direct analysis in real time-high resolution mass spectrometry-derived "neutral loss" spectra.

PubMed

Fowble, Kristen L; Shepard, Jason R E; Musah, Rabi A

2018-03-01

An approach to the rapid determination of the structures of novel synthetic cathinone designer drugs, also known as bath salts, is reported. While cathinones fragment so extensively by electron impact mass spectrometry that their mass spectra often cannot be used to identify the structure, collision-induced dissociation (CID) direct analysis in real time-high resolution mass spectrometry (DART-HRMS) experiments furnished spectra that provided diagnostic fragmentation patterns for the analyzed cathinones. From this data, neutral loss spectra, which reflect the presence of specific chemical moieties, could be acquired. These spectra showed striking similarities between cathinones sharing structural features such as pyrrolidine rings and methylenedioxy moieties. Principle component analysis (PCA) of the neutral loss spectra of nine synthetic cathinones of various types including ethcathinones, those containing a methylenedioxy moiety appended to the benzene ring, and pyrrolidine-containing structures, illustrated that cathinones falling within the same class clustered together and could be distinguished from those of other classes. Furthermore, hierarchical clustering analysis of the neutral loss data of a model set derived from 44 synthetic cathinones, furnished a dendrogram in which structurally similar cathinones clustered together. The ability of this model system to facilitate structure determination was tested using 4-fluoroethcathinone, 3,4-methylenedioxy-α-pyrrolidinohexanophenone (MDPHP), and ethylone, which fall into the ethcathinone, pyrrolidine-containing, and methylenedioxy-containing subclasses respectively. The results showed that their neutral loss spectra correctly fell within the ethcathinone, pyrrolidine-containing and methylenedioxy-containing cathinone clades of the dendrogram, and that the neutral loss information could be used to infer the structures of these compounds. The analysis and data processing steps are rapid and samples can be analyzed in their native form without any sample processing steps. The robustness of the dendrogram dataset can be readily increased by continued addition of newly discovered structures. The approach can be broadly applied to structure determination of unknowns, and would be particularly useful for analyses where sample amounts are limited. Copyright © 2017 Elsevier B.V. All rights reserved.

Expressed Glycosylphosphatidylinositol-Anchored Horseradish Peroxidase Identifies Co-Clustering Molecules in Individual Lipid Raft Domains

PubMed Central

Miyagawa-Yamaguchi, Arisa; Kotani, Norihiro; Honke, Koichi

2014-01-01

Lipid rafts that are enriched in glycosylphosphatidylinositol (GPI)-anchored proteins serve as a platform for important biological events. To elucidate the molecular mechanisms of these events, identification of co-clustering molecules in individual raft domains is required. Here we describe an approach to this issue using the recently developed method termed enzyme-mediated activation of radical source (EMARS), by which molecules in the vicinity within 300 nm from horseradish peroxidase (HRP) set on the probed molecule are labeled. GPI-anchored HRP fusion proteins (HRP-GPIs), in which the GPI attachment signals derived from human decay accelerating factor and Thy-1 were separately connected to the C-terminus of HRP, were expressed in HeLa S3 cells, and the EMARS reaction was catalyzed by these expressed HRP-GPIs under a living condition. As a result, these different HRP-GPIs had differences in glycosylation and localization and formed distinct clusters. This novel approach distinguished molecular clusters associated with individual GPI-anchored proteins, suggesting that it can identify co-clustering molecules in individual raft domains. PMID:24671047

The globular cluster NGC 7492 and the Sagittarius tidal stream: together but unmixed

NASA Astrophysics Data System (ADS)

Carballo-Bello, J. A.; Corral-Santana, J. M.; Catelan, M.; Martínez-Delgado, D.; Muñoz, R. R.; Sollima, A.; Navarrete, C.; Duffau, S.; Côté, P.; Mora, M. D.

2018-03-01

We have derived from VIMOS spectroscopy the radial velocities for a sample of 71 stars selected from CFHT/Megacam photometry around the Galactic globular cluster NGC 7492. In the resulting velocity distribution, it is possible to distinguish two relevant non-Galactic kinematic components along the same line of sight: a group of stars at 〈vr〉 ˜ 125 km s-1 which is compatible with the velocity of the old leading arm of the Sagittarius tidal stream, and a larger number of objects at 〈vr〉 ˜ -110 km s-1 that might be identified as members of the trailing wrap of the same stream. The systemic velocity of NGC 7492 set at vr ˜ -177 km s-1 differs significantly from that of both components, thus our results confirm that this cluster is not one of the globular clusters deposited by the Sagittarius dwarf spheroidal in the Galactic halo, even if it is immersed in the stream. A group of stars with 〈vr〉 ˜ - 180 km s-1 might be comprised of cluster members along one of the tidal tails of NGC 7492.

Effects of Combined Stellar Feedback on Star Formation in Stellar Clusters

NASA Astrophysics Data System (ADS)

Wall, Joshua Edward; McMillan, Stephen; Pellegrino, Andrew; Mac Low, Mordecai; Klessen, Ralf; Portegies Zwart, Simon

2018-01-01

We present results of hybrid MHD+N-body simulations of star cluster formation and evolution including self consistent feedback from the stars in the form of radiation, winds, and supernovae from all stars more massive than 7 solar masses. The MHD is modeled with the adaptive mesh refinement code FLASH, while the N-body computations are done with a direct algorithm. Radiation is modeled using ray tracing along long characteristics in directions distributed using the HEALPIX algorithm, and causes ionization and momentum deposition, while winds and supernova conserve momentum and energy during injection. Stellar evolution is followed using power-law fits to evolution models in SeBa. We use a gravity bridge within the AMUSE framework to couple the N-body dynamics of the stars to the gas dynamics in FLASH. Feedback from the massive stars alters the structure of young clusters as gas ejection occurs. We diagnose this behavior by distinguishing between fractal distribution and central clustering using a Q parameter computed from the minimum spanning tree of each model cluster. Global effects of feedback in our simulations will also be discussed.

Seed and soil dynamics in shrubland ecosystems: proceedings; 2002 August 12-16; Laramie, WY

Treesearch

Ann L. Hild; Nancy L. Shaw; Susan E. Meyer; D. Terrance Booth; E. Durant McArthur

2004-01-01

The 38 papers in this proceedings are divided into six sections; the first includes an overview paper and documentation of the first Shrub Research Consortium Distinguished Service Award. The next four sections cluster papers on restoration and revegetation, soil and microsite requirements, germination and establishment of desired species, and community ecology of...

[Ultrastructure and Raman Spectral Characteristics of Two Kinds of Acute Myeloid Leukemia Cells].

PubMed

Liang, Hao-Yue; Cheng, Xue-Lian; Dong, Shu-Xu; Zhao, Shi-Xuan; Wang, Ying; Ru, Yong-Xin

2018-02-01

To investigate the Raman spectral characteristics of leukemia cells from 4 patients with acute promyelocytic leukemia (M 3 ) and 3 patients with acute monoblastic leukemia (M 5 ), establish a novel Raman label-free method to distinguish 2 kinds of acute myeloid leukemia cells so as to provide basis for clinical research. Leukemia cells were collected from bone marrow of above-mentioned patients. Raman spectra were acquired by Horiba Xplora Raman spectrometer and Raman spectra of 30-50 cells from each patient were recorded. The diagnostic model was established according to principle component analysis (PCA), discriminant function analysis (DFA) and cluster analysis, and the spectra of leukemia cells from 7 patients were analyzed and classified. Characteristics of Raman spectra were analyzed combining with ultrastructure of leukemia cells. There were significant differences between Raman spectra of 2 kinds of leukemia cells. Compared with acute monoblastic leukemia cells, the spectra of acute promyelocytic leukemia cells showed stronger peaks in 622, 643, 757, 852, 1003, 1033, 1117, 1157, 1173, 1208, 1340, 1551, 1581 cm -1 . The diagnostic models established by PCA-DFA and cluster analysis could successfully classify these Raman spectra of different samples with a high accuracy of 100% (233/233). The model was evaluated by "Leave-one-out" cross-validation and reached a high accuracy of 97% (226/233). The level of macromolecules of M 3 cells is higher than that of M 5 . The diagnostic models established by PCA-DFA can classify these Raman spectra of different cells with a high accuracy. Raman spectra shows consistent result with ultrastructure by TEM.

Small-scale Conformity of the Virgo Cluster Galaxies

NASA Astrophysics Data System (ADS)

Lee, Hye-Ran; Lee, Joon Hyeop; Jeong, Hyunjin; Park, Byeong-Gon

2016-06-01

We investigate the small-scale conformity in color between bright galaxies and their faint companions in the Virgo Cluster. Cluster member galaxies are spectroscopically determined using the Extended Virgo Cluster Catalog and the Sloan Digital Sky Survey Data Release 12. We find that the luminosity-weighted mean color of faint galaxies depends on the color of adjacent bright galaxy as well as on the cluster-scale environment (gravitational potential index). From this result for the entire area of the Virgo Cluster, it is not distinguishable whether the small-scale conformity is genuine or if it is artificially produced due to cluster-scale variation of galaxy color. To disentangle this degeneracy, we divide the Virgo Cluster area into three sub-areas so that the cluster-scale environmental dependence is minimized: A1 (central), A2 (intermediate), and A3 (outermost). We find conformity in color between bright galaxies and their faint companions (color-color slope significance S ˜ 2.73σ and correlation coefficient {cc}˜ 0.50) in A2, where the cluster-scale environmental dependence is almost negligible. On the other hand, the conformity is not significant or very marginal (S ˜ 1.75σ and {cc}˜ 0.27) in A1. The conformity is not significant either in A3 (S ˜ 1.59σ and {cc}˜ 0.44), but the sample size is too small in this area. These results are consistent with a scenario in which the small-scale conformity in a cluster is a vestige of infallen groups and these groups lose conformity as they come closer to the cluster center.

Genome-wide identification and expression analysis of the ClTCP transcription factors in Citrullus lanatus.

PubMed

Shi, Pibiao; Guy, Kateta Malangisha; Wu, Weifang; Fang, Bingsheng; Yang, Jinghua; Zhang, Mingfang; Hu, Zhongyuan

2016-04-12

The plant-specific TCP transcription factor family, which is involved in the regulation of cell growth and proliferation, performs diverse functions in multiple aspects of plant growth and development. However, no comprehensive analysis of the TCP family in watermelon (Citrullus lanatus) has been undertaken previously. A total of 27 watermelon TCP encoding genes distributed on nine chromosomes were identified. Phylogenetic analysis clustered the genes into 11 distinct subgroups. Furthermore, phylogenetic and structural analyses distinguished two homology classes within the ClTCP family, designated Class I and Class II. The Class II genes were differentiated into two subclasses, the CIN subclass and the CYC/TB1 subclass. The expression patterns of all members were determined by semi-quantitative PCR. The functions of two ClTCP genes, ClTCP14a and ClTCP15, in regulating plant height were confirmed by ectopic expression in Arabidopsis wild-type and ortholog mutants. This study represents the first genome-wide analysis of the watermelon TCP gene family, which provides valuable information for understanding the classification and functions of the TCP genes in watermelon.

Sensitivity Analysis of Multiple Informant Models When Data are Not Missing at Random

PubMed Central

Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae; Scaramella, Laura; Leve, Leslie; Reiss, David

2014-01-01

Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups may be retained even if only one member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that may also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches may result in a data analysis problem for which the missingness is ignorable. This paper considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates to assumptions about missing data, a strategy that may be easily implemented using SEM software. PMID:25221420

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

PubMed

Yokoyama, Eiji; Uchimura, Masako

2007-11-01

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

Statistics of voids in hierarchical universes

NASA Technical Reports Server (NTRS)

Fry, J. N.

1986-01-01

As one alternative to the N-point galaxy correlation function statistics, the distribution of holes or the probability that a volume of given size and shape be empty of galaxies can be considered. The probability of voids resulting from a variety of hierarchical patterns of clustering is considered, and these are compared with the results of numerical simulations and with observations. A scaling relation required by the hierarchical pattern of higher order correlation functions is seen to be obeyed in the simulations, and the numerical results show a clear difference between neutrino models and cold-particle models; voids are more likely in neutrino universes. Observational data do not yet distinguish but are close to being able to distinguish between models.

Seismicity and source spectra analysis in Salton Sea Geothermal Field

NASA Astrophysics Data System (ADS)

Cheng, Y.; Chen, X.

2016-12-01

The surge of "man-made" earthquakes in recent years has led to considerable concerns about the associated hazards. Improved monitoring of small earthquakes would significantly help understand such phenomena and the underlying physical mechanisms. In the Salton Sea Geothermal field in southern California, open access of a local borehole network provides a unique opportunity to better understand the seismicity characteristics, the related earthquake hazards, and the relationship with the geothermal system, tectonic faulting and other physical conditions. We obtain high-resolution earthquake locations in the Salton Sea Geothermal Field, analyze characteristics of spatiotemporal isolated earthquake clusters, magnitude-frequency distributions and spatial variation of stress drops. The analysis reveals spatial coherent distributions of different types of clustering, b-value distributions, and stress drop distribution. The mixture type clusters (short-duration rapid bursts with high aftershock productivity) are predominately located within active geothermal field that correlate with high b-value, low stress drop microearthquake clouds, while regular aftershock sequences and swarms are distributed throughout the study area. The differences between earthquakes inside and outside of geothermal operation field suggest a possible way to distinguish directly induced seismicity due to energy operation versus typical seismic slip driven sequences. The spatial coherent b-value distribution enables in-situ estimation of probabilities for M≥3 earthquakes, and shows that the high large-magnitude-event (LME) probability zones with high stress drop are likely associated with tectonic faulting. The high stress drop in shallow (1-3 km) depth indicates the existence of active faults, while low stress drops near injection wells likely corresponds to the seismic response to fluid injection. I interpret the spatial variation of seismicity and source characteristics as the result of fluid circulation, the fracture network, and tectonic faulting.

The Methanosarcina barkeri genome: comparative analysis withMethanosarcina acetivorans and Methanosarcina mazei reveals extensiverearrangement within methanosarcinal genomes

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

Maeder, Dennis L.; Anderson, Iain; Brettin, Thomas S.

2006-05-19

We report here a comparative analysis of the genome sequence of Methanosarcina barkeri with those of Methanosarcina acetivorans and Methanosarcina mazei. All three genomes share a conserved double origin of replication and many gene clusters. M. barkeri is distinguished by having an organization that is well conserved with respect to the other Methanosarcinae in the region proximal to the origin of replication with interspecies gene similarities as high as 95%. However it is disordered and marked by increased transposase frequency and decreased gene synteny and gene density in the proximal semi-genome. Of the 3680 open reading frames in M. barkeri,more » 678 had paralogs with better than 80% similarity to both M. acetivorans and M. mazei while 128 nonhypothetical orfs were unique (non-paralogous) amongst these species including a complete formate dehydrogenase operon, two genes required for N-acetylmuramic acid synthesis, a 14 gene gas vesicle cluster and a bacterial P450-specific ferredoxin reductase cluster not previously observed or characterized in this genus. A cryptic 36 kbp plasmid sequence was detected in M. barkeri that contains an orc1 gene flanked by a presumptive origin of replication consisting of 38 tandem repeats of a 143 nt motif. Three-way comparison of these genomes reveals differing mechanisms for the accrual of changes. Elongation of the large M. acetivorans is the result of multiple gene-scale insertions and duplications uniformly distributed in that genome, while M. barkeri is characterized by localized inversions associated with the loss of gene content. In contrast, the relatively short M. mazei most closely approximates the ancestral organizational state.« less

Effect of two different treatments for reducing grape yield in Vitis vinifera cv Syrah on wine composition and quality: berry thinning versus cluster thinning.

PubMed

Gil, M; Esteruelas, M; González, E; Kontoudakis, N; Jiménez, J; Fort, F; Canals, J M; Hermosín-Gutiérrez, I; Zamora, F

2013-05-22

The influence of two treatments for reducing grape yield, cluster thinning and berry thinning, on red wine composition and quality were studied in a Vitis vinifera cv Syrah vineyard in AOC Penedès (Spain). Cluster thinning reduced grape yield per vine by around 40% whereas berry thinning only reduced it by around 20%. Cluster thinning grapes had higher soluble solids content than control grapes, and their resultant wines have greater anthocyanin and polysaccharide concentrations than the control wine. Wine obtained from berry thinning grapes had a higher total phenolic index, greater flavonol, proanthocyanidin, and polysaccharide concentrations, and lower titratable acidity than the control wine. Wines obtained from both treatments were sufficiently different from the control wine to be significantly distinguished by a trained panel in a triangular test. Even though both treatments seem to be effective at improving the quality of wine, berry thinning has the advantage because it has less impact on crop yield reduction.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

PubMed

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

PubMed Central

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed. PMID:25972896

The velocity field of clusters of galaxies within 100 megaparsecs. II - Northern clusters

NASA Technical Reports Server (NTRS)

Mould, J. R.; Akeson, R. L.; Bothun, G. D.; Han, M.; Huchra, J. P.; Roth, J.; Schommer, R. A.

1993-01-01

Distances and peculiar velocities for galaxies in eight clusters and groups have been determined by means of the near-infrared Tully-Fisher relation. With the possible exception of a group halfway between us and the Hercules Cluster, we observe peculiar velocities of the same order as the measuring errors of about 400 km/s. The present sample is drawn from the northern Galactic hemisphere and delineates a quiet region in the Hubble flow. This contrasts with the large-scale flows seen in the Hydra-Centaurus and Perseus-Pisces regions. We compare the observed peculiar velocities with predictions based upon the gravity field inferred from the IRAS redshift survey. The differences between the observed and predicted peculiar motions are generally small, except near dense structures, where the observed motions exceed the predictions by significant amounts. Kinematic models of the velocity field are also compared with the data. We cannot distinguish between parameterized models with a great attractor or models with a bulk flow.

Induced clustering of Escherichia coli by acoustic fields.

PubMed

Gutiérrez-Ramos, Salomé; Hoyos, Mauricio; Ruiz-Suárez, J C

2018-03-16

Brownian or self-propelled particles in aqueous suspensions can be trapped by acoustic fields generated by piezoelectric transducers usually at frequencies in the megahertz. The obtained confinement allows the study of rich collective behaviours like clustering or spreading dynamics in microgravity-like conditions. The acoustic field induces the levitation of self-propelled particles and provides secondary lateral forces to capture them at nodal planes. Here, we give a step forward in the field of confined active matter, reporting levitation experiments of bacterial suspensions of Escherichia coli. Clustering of living bacteria is monitored as a function of time, where different behaviours are clearly distinguished. Upon the removal of the acoustic signal, bacteria rapidly spread, impelled by their own swimming. Nevertheless, long periods of confinement result in irreversible bacteria entanglements that could act as seeds for levitating bacterial aggregates.

A homeotic gene cluster patterns the anteroposterior body axis of C. elegans.

PubMed

Wang, B B; Müller-Immergluck, M M; Austin, J; Robinson, N T; Chisholm, A; Kenyon, C

1993-07-16

In insects and vertebrates, clusters of Antennapedia class homeobox (HOM-C) genes specify anteroposterior body pattern. The nematode C. elegans also contains a small cluster of HOM-C genes, one of which has been shown to specify positional identity. Here we show that two additional C. elegans HOM-C genes also specify positional identity and that together these three HOM-C genes function along the anteroposterior axis in the same order as their homologs in other organisms. Thus, HOM-C-based pattern formation has been conserved in nematodes despite the many differences in morphology and embryology that distinguish them from other phyla. Each C. elegans HOM-C gene is responsible for a distinct body region; however, where their domains overlap, two HOM-C genes can act together to specify the fates of individual cells.

[The psychosocial status of patients with endogenous eczema. A study using cluster analysis for the correlation of psychological factors with somatic findings].

PubMed

Gieler, U; Ehlers, A; Höhler, T; Burkard, G

1990-08-01

The present study was performed to investigate whether patients with atopic dermatitis differ as a group from controls on psychological measures of mood and personality or whether psychologically deviant and normal patient subgroups can be distinguished. Furthermore, we were interested in what clinical characteristics might co-vary with psychological disability in patients with atopic dermatitis. In all, 93 patients filled in a standardized mood scale (Hamburg-Erlanger-Stimmungsbarometer) and a personality scale (Kurztest zur Erfassung der Persönlichkeitsstruktur). Compared with matched controls, patients described themselves as being more anxious, more aroused, more depressed and less energetic, and they reached higher neuroticism scores. A cluster analysis identified four patient subgroups. Only one of the subgroups (n = 17) was psychologically disabled according to the questionnaire scores. In contrast to a psychologically stabile patient group, the psychologically disabled patients showed an earlier age of onset of dermatitis, but less intense itching and scratching. They reported more somatic complaints and a higher level of familial stress, were more dissatisfied with their life situation and work, had fewer friends and experienced more losses of significant others. Furthermore, they more frequently rated their disorder as being determined by psychological factors and were more intelligent. Thus, the questionnaires identified a subgroup of patients who may need psychotherapeutic interventions.

[Investigation of metabolic action of Cordyceps sinensis and its cultured mycelia on Escherichia coli by microcalorimetry].

PubMed

Zhou, Dan-Lei; Yan, Dan; Li, Bao-Cai; Wu, Yan-Shu; Xiao, Xiao-He

2009-06-01

This study is to investigate the effect of Cordyceps sinensis and its cultured mycelia on growth and metabolism of Escherichia coli, and microcalorimetric method was carried out to evaluate its biological activity. The study will provide the basis for the quality control of Cordyceps sinensis. Experimental result will show the effect of natural Cordyceps sinensis and its cultured mycelia on growth and metabolism of Escherichia coli, with index of P(1max) and effective rate (E) by microcalorimetry, the data of experiment were studied by cluster analysis. The results showed that Cordyceps sinensis and its cultured mycelia not only can promote growth and metabolism of Escherichia coli but also can regulate the balance of intestinal microecology efficiently. When the concentrations of samples > 6.0 mg mL(-1), natural Cordyceps sinensis can promote the growth and metabolism of Escherichia coli efficiently (P < 0.05) compared with the control group, and have better dose-effect relationship with concentration (r > 0.9), its cultured mycelia does not show conspicuous auxoaction (P > 0.05) and have not dose-effect relationship with concentration (r < 0.6); when the concentration of samples < 6.0 mg mL(-1), all samples does not show conspicuous auxoaction (P > 0.05). Natural Cordyceps sinensis and its cultured mycelia can be distinguished by cluster analysis. Microcalorimetry has a good prospect on the quality evaluation of the traditional Chinese medicine.

Segmentation of overweight Americans and opportunities for social marketing

PubMed Central

Kolodinsky, Jane; Reynolds, Travis

2009-01-01

Background The food industry uses market segmentation to target products toward specific groups of consumers with similar attitudinal, demographic, or lifestyle characteristics. Our aims were to identify distinguishable segments within the US overweight population to be targeted with messages and media aimed at moving Americans toward more healthy weights. Methods Cluster analysis was used to identify segments of consumers based on both food and lifestyle behaviors related to unhealthy weights. Drawing from Social Learning Theory, the Health Belief Model, and existing market segmentation literature, the study identified five distinct, recognizable market segments based on knowledge and behavioral and environmental factors. Implications for social marketing campaigns designed to move Americans toward more healthy weights were explored. Results The five clusters identified were: Highest Risk (19%); At Risk (22%); Right Behavior/Wrong Results (33%); Getting Best Results (13%); and Doing OK (12%). Ninety-nine percent of those in the Highest Risk cluster were overweight; members watched the most television and exercised the least. Fifty-five percent of those in the At Risk cluster were overweight; members logged the most computer time and almost half rarely or never read food labels. Sixty-six percent of those in the Right Behavior/Wrong Results cluster were overweight; however, 95% of them were familiar with the food pyramid. Members reported eating a low percentage of fast food meals (8%) compared to other groups but a higher percentage of other restaurant meals (15%). Less than six percent of those in the Getting Best Results cluster were overweight; every member read food labels and 75% of members' meals were "made from scratch." Eighteen percent of those in the Doing OK cluster were overweight; members watched the least television and reported eating 78% of their meals "made from scratch." Conclusion This study demonstrated that five distinct market segments can be identified for social marketing efforts aimed at addressing the obesity epidemic. Through the identification of these five segments, social marketing campaigns can utilize selected channels and messages that communicate the most relevant and important information. The results of this study offer insight into how segmentation strategies and social marketing messages may improve public health. PMID:19267936

Segmentation of overweight Americans and opportunities for social marketing.

PubMed

Kolodinsky, Jane; Reynolds, Travis

2009-03-08

The food industry uses market segmentation to target products toward specific groups of consumers with similar attitudinal, demographic, or lifestyle characteristics. Our aims were to identify distinguishable segments within the US overweight population to be targeted with messages and media aimed at moving Americans toward more healthy weights. Cluster analysis was used to identify segments of consumers based on both food and lifestyle behaviors related to unhealthy weights. Drawing from Social Learning Theory, the Health Belief Model, and existing market segmentation literature, the study identified five distinct, recognizable market segments based on knowledge and behavioral and environmental factors. Implications for social marketing campaigns designed to move Americans toward more healthy weights were explored. The five clusters identified were: Highest Risk (19%); At Risk (22%); Right Behavior/Wrong Results (33%); Getting Best Results (13%); and Doing OK (12%). Ninety-nine percent of those in the Highest Risk cluster were overweight; members watched the most television and exercised the least. Fifty-five percent of those in the At Risk cluster were overweight; members logged the most computer time and almost half rarely or never read food labels. Sixty-six percent of those in the Right Behavior/Wrong Results cluster were overweight; however, 95% of them were familiar with the food pyramid. Members reported eating a low percentage of fast food meals (8%) compared to other groups but a higher percentage of other restaurant meals (15%). Less than six percent of those in the Getting Best Results cluster were overweight; every member read food labels and 75% of members' meals were "made from scratch." Eighteen percent of those in the Doing OK cluster were overweight; members watched the least television and reported eating 78% of their meals "made from scratch." This study demonstrated that five distinct market segments can be identified for social marketing efforts aimed at addressing the obesity epidemic. Through the identification of these five segments, social marketing campaigns can utilize selected channels and messages that communicate the most relevant and important information. The results of this study offer insight into how segmentation strategies and social marketing messages may improve public health.

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

PubMed

Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

Identification of Neoceratitis asiatica (Becker) (Diptera: Tephritidae) based on morphological characteristics and DNA barcode.

PubMed

Guo, Shaokun; He, Jia; Zhao, Zihua; Liu, Lijun; Gao, Liyuan; Wei, Shuhua; Guo, Xiaoyu; Zhang, Rong; Li, Zhihong

2017-12-12

Neoceratitis asiatica (Becker), which especially infests wolfberry (Lycium barbarum L.), could cause serious economic losses every year in China, especially to organic wolfberry production. In some important wolfberry plantings, it is difficult and time-consuming to rear the larvae or pupae to adults for morphological identification. Molecular identification based on DNA barcode is a solution to the problem. In this study, 15 samples were collected from Ningxia, China. Among them, five adults were identified according to their morphological characteristics. The utility of mitochondrial DNA (mtDNA) cytochrome c oxidase I (COI) gene sequence as DNA barcode in distinguishing N. asiatica was evaluated by analysing Kimura 2-parameter distances and phylogenetic trees. There were significant differences between intra-specific and inter-specific genetic distances according to the barcoding gap analysis. The uncertain larval and pupal samples were within the same cluster as N. asiatica adults and formed sister cluster to N. cyanescens. A combination of morphological and molecular methods enabled accurate identification of N. asiatica. This is the first study using DNA barcode to identify N. asiatica and the obtained DNA sequences will be added to the DNA barcode database.

Equilibrium relations and bipolar cognitive mapping for online analytical processing with applications in international relations and strategic decision support.

PubMed

Zhang, Wen-Ran

2003-01-01

Bipolar logic, bipolar sets, and equilibrium relations are proposed for bipolar cognitive mapping and visualization in online analytical processing (OLAP) and online analytical mining (OLAM). As cognitive models, cognitive maps (CMs) hold great potential for clustering and visualization. Due to the lack of a formal mathematical basis, however, CM-based OLAP and OLAM have not gained popularity. Compared with existing approaches, bipolar cognitive mapping has a number of advantages. First, bipolar CMs are formal logical models as well as cognitive models. Second, equilibrium relations (with polarized reflexivity, symmetry, and transitivity), as bipolar generalizations and fusions of equivalence relations, provide a theoretical basis for bipolar visualization and coordination. Third, an equilibrium relation or CM induces bipolar partitions that distinguish disjoint coalition subsets not involved in any conflict, disjoint coalition subsets involved in a conflict, disjoint conflict subsets, and disjoint harmony subsets. Finally, equilibrium energy analysis leads to harmony and stability measures for strategic decision and multiagent coordination. Thus, this work bridges a gap for CM-based clustering and visualization in OLAP and OLAM. Basic ideas are illustrated with example CMs in international relations.

H-alpha LEGUS: Insights into the Field OB Star Population in Nearby Galaxies

NASA Astrophysics Data System (ADS)

Lee, Janice; Thilker, David; Kayitesi, Bridget; Chandar, Rupali; Halpha LEGUS Team

2018-01-01

The question of whether O-stars can form in isolation, without attendant clusters or associations of lower mass stars, is a topic of interest because the answer to the question can distinguish between models of star formation. To begin to investigate whether such isolated O-stars can be identified in nearby galaxies beyond the Local Group, we identify candidate field OB-stars in NGC 1313, NGC 4395 and NGC 7793, the three nearest spiral galaxies in the HST Legacy ExtraGalactic Ultraviolet Survey (LEGUS). Candidates are selected using a technique based on: (1) a reddening-free Q parameter, adapted for photometry in HST filters covering the NUV, U, & B bands; (2) isolation based on projected distance from the nearest young cluster and candidate OB star, and (3) the presence of an HII region, identified based on HST H-alpha narrowband imaging. Our catalogs enable a range of follow-up studies on massive stars, and in particular provide targets for future spectroscopic observation and analysis. We describe the candidate OB star sample, the spatial distribution of the stars, and their HII region properties, with special focus on the most isolated objects in the sample.

On the physical nature of globular cluster candidates in the Milky Way bulge

NASA Astrophysics Data System (ADS)

Piatti, Andrés E.

2018-06-01

We present results from 2MASS JKs photometry on the physical reality of recently reported globular cluster (GC) candidates in the Milky Way (MW) bulge. We relied our analysis on photometric membership probabilities that allowed us to distinguish real stellar aggregates from the composite field star population. When building colour-magnitude diagrams and stellar density maps for stars at different membership probability levels, the genuine GC candidate populations are clearly highlighted. We then used the tip of the red giant branch (RGB) as distance estimator, resulting in heliocentric distances that place many of the objects in regions near the MW bulge, where no GC had been previously recognized. Some few GC candidates resulted to be MW halo/disc objects. Metallicities estimated from the standard RGB method are in agreement with the values expected according to the position of the GC candidates in the Galaxy. Finally, we derived, for the first time, their structural parameters. We found that the studied objects have core, half-light, and tidal radii in the ranges spanned by the population of known MW GCs. Their internal dynamical evolutionary stages will be described properly when their masses are estimated.

Retrotransposon-Based Molecular Markers for Analysis of Genetic Diversity within the Genus Linum

PubMed Central

Melnikova, Nataliya V.; Kudryavtseva, Anna V.; Zelenin, Alexander V.; Lakunina, Valentina A.; Yurkevich, Olga Yu.; Speranskaya, Anna S.; Dmitriev, Alexey A.; Krinitsina, Anastasia A.; Belenikin, Maxim S.; Uroshlev, Leonid A.; Snezhkina, Anastasiya V.; Sadritdinova, Asiya F.; Koroban, Nadezda V.; Amosova, Alexandra V.; Samatadze, Tatiana E.; Guzenko, Elena V.; Lemesh, Valentina A.; Savilova, Anastasya M.; Rachinskaia, Olga A.; Kishlyan, Natalya V.; Rozhmina, Tatiana A.; Bolsheva, Nadezhda L.; Muravenko, Olga V.

2014-01-01

SSAP method was used to study the genetic diversity of 22 Linum species from sections Linum, Adenolinum, Dasylinum, Stellerolinum, and 46 flax cultivars. All the studied flax varieties were distinguished using SSAP for retrotransposons FL9 and FL11. Thus, the validity of SSAP method was demonstrated for flax marking, identification of accessions in genebank collections, and control during propagation of flax varieties. Polymorphism of Fl1a, Fl1b, and Cassandra insertions were very low in flax varieties, but these retrotransposons were successfully used for the investigation of Linum species. Species clusterization based on SSAP markers was in concordance with their taxonomic division into sections Dasylinum, Stellerolinum, Adenolinum, and Linum. All species of sect. Adenolinum clustered apart from species of sect. Linum. The data confirmed the accuracy of the separation in these sections. Members of section Linum are not as closely related as members of other sections, so taxonomic revision of this section is desirable. L. usitatissimum accessions genetically distant from modern flax cultivars were revealed in our work. These accessions are of utmost interest for flax breeding and introduction of new useful traits into flax cultivars. The chromosome localization of Cassandra retrotransposon in Linum species was determined. PMID:25243121

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

Context Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results A clustering analysis based only on the J = 1 – 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes (nH ~ 100 cm−3, ~ 500 cm−3, and > 1000 cm−3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 − 0 line of HCO+ and the N = 1 − 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH ~ 7 × 103 cm−3 ~ 4 × 104 cm−3) for the 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

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level.

PubMed

Tomás, Inmaculada; Regueira-Iglesias, Alba; López, Maria; Arias-Bujanda, Nora; Novoa, Lourdes; Balsa-Castro, Carlos; Tomás, Maria

2017-01-01

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa) , Fusobacterium nucleatum (Fn) , Parvimonas micra (Pm) , Porphyromonas gingivalis (Pg) , Prevotella intermedia (Pi) , Tannerella forsythia (Tf) , and Treponema denticola (Td) . The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky's complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa , while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis.

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

PubMed Central

Tomás, Inmaculada; Regueira-Iglesias, Alba; López, Maria; Arias-Bujanda, Nora; Novoa, Lourdes; Balsa-Castro, Carlos; Tomás, Maria

2017-01-01

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky’s complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis. PMID:28848499

Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees.

PubMed

Kastberger, Gerald; Maurer, Michael; Weihmann, Frank; Ruether, Matthias; Hoetzl, Thomas; Kranner, Ilse; Bischof, Horst

2011-02-08

The detailed interpretation of mass phenomena such as human escape panic or swarm behaviour in birds, fish and insects requires detailed analysis of the 3D movements of individual participants. Here, we describe the adaptation of a 3D stereoscopic imaging method to measure the positional coordinates of individual agents in densely packed clusters. The method was applied to study behavioural aspects of shimmering in Giant honeybees, a collective defence behaviour that deters predatory wasps by visual cues, whereby individual bees flip their abdomen upwards in a split second, producing Mexican wave-like patterns. Stereoscopic imaging provided non-invasive, automated, simultaneous, in-situ 3D measurements of hundreds of bees on the nest surface regarding their thoracic position and orientation of the body length axis. Segmentation was the basis for the stereo matching, which defined correspondences of individual bees in pairs of stereo images. Stereo-matched "agent bees" were re-identified in subsequent frames by the tracking procedure and triangulated into real-world coordinates. These algorithms were required to calculate the three spatial motion components (dx: horizontal, dy: vertical and dz: towards and from the comb) of individual bees over time. The method enables the assessment of the 3D positions of individual Giant honeybees, which is not possible with single-view cameras. The method can be applied to distinguish at the individual bee level active movements of the thoraces produced by abdominal flipping from passive motions generated by the moving bee curtain. The data provide evidence that the z-deflections of thoraces are potential cues for colony-intrinsic communication. The method helps to understand the phenomenon of collective decision-making through mechanoceptive synchronization and to associate shimmering with the principles of wave propagation. With further, minor modifications, the method could be used to study aspects of other mass phenomena that involve active and passive movements of individual agents in densely packed clusters.

Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees

PubMed Central

2011-01-01

Background The detailed interpretation of mass phenomena such as human escape panic or swarm behaviour in birds, fish and insects requires detailed analysis of the 3D movements of individual participants. Here, we describe the adaptation of a 3D stereoscopic imaging method to measure the positional coordinates of individual agents in densely packed clusters. The method was applied to study behavioural aspects of shimmering in Giant honeybees, a collective defence behaviour that deters predatory wasps by visual cues, whereby individual bees flip their abdomen upwards in a split second, producing Mexican wave-like patterns. Results Stereoscopic imaging provided non-invasive, automated, simultaneous, in-situ 3D measurements of hundreds of bees on the nest surface regarding their thoracic position and orientation of the body length axis. Segmentation was the basis for the stereo matching, which defined correspondences of individual bees in pairs of stereo images. Stereo-matched "agent bees" were re-identified in subsequent frames by the tracking procedure and triangulated into real-world coordinates. These algorithms were required to calculate the three spatial motion components (dx: horizontal, dy: vertical and dz: towards and from the comb) of individual bees over time. Conclusions The method enables the assessment of the 3D positions of individual Giant honeybees, which is not possible with single-view cameras. The method can be applied to distinguish at the individual bee level active movements of the thoraces produced by abdominal flipping from passive motions generated by the moving bee curtain. The data provide evidence that the z-deflections of thoraces are potential cues for colony-intrinsic communication. The method helps to understand the phenomenon of collective decision-making through mechanoceptive synchronization and to associate shimmering with the principles of wave propagation. With further, minor modifications, the method could be used to study aspects of other mass phenomena that involve active and passive movements of individual agents in densely packed clusters. PMID:21303539

What Constitutes Adoption of the Web: A Methodological Problem in Assessing Adoption of the World Wide Web for Electronic Commerce.

ERIC Educational Resources Information Center

White, Marilyn Domas; Abels, Eileen G.; Gordon-Murnane, Laura

1998-01-01

Reports on methodological developments in a project to assess the adoption of the Web by publishers of business information for electronic commerce. Describes the approach used on a sample of 20 business publishers to identify five clusters of publishers ranging from traditionalist to innovator. Distinguishes between adopters and nonadopters of…

Neural correlates of wishful thinking.

PubMed

Aue, Tatjana; Nusbaum, Howard C; Cacioppo, John T

2012-11-01

Wishful thinking (WT) implies the overestimation of the likelihood of desirable events. It occurs for outcomes of personal interest, but also for events of interest to others we like. We investigated whether WT is grounded on low-level selective attention or on higher level cognitive processes including differential weighting of evidence or response formation. Participants in our MRI study predicted the likelihood that their favorite or least favorite team would win a football game. Consistent with expectations, favorite team trials were characterized by higher winning odds. Our data demonstrated activity in a cluster comprising parts of the left inferior occipital and fusiform gyri to distinguish between favorite and least favorite team trials. More importantly, functional connectivities of this cluster with the human reward system were specifically involved in the type of WT investigated in our study, thus supporting the idea of an attention bias generating WT. Prefrontal cortex activity also distinguished between the two teams. However, activity in this region and its functional connectivities with the human reward system were altogether unrelated to the degree of WT reflected in the participants' behavior and may rather be related to social identification, ensuring the affective context necessary for WT to arise.

Neural correlates of wishful thinking

PubMed Central

Nusbaum, Howard C.; Cacioppo, John T.

2012-01-01

Wishful thinking (WT) implies the overestimation of the likelihood of desirable events. It occurs for outcomes of personal interest, but also for events of interest to others we like. We investigated whether WT is grounded on low-level selective attention or on higher level cognitive processes including differential weighting of evidence or response formation. Participants in our MRI study predicted the likelihood that their favorite or least favorite team would win a football game. Consistent with expectations, favorite team trials were characterized by higher winning odds. Our data demonstrated activity in a cluster comprising parts of the left inferior occipital and fusiform gyri to distinguish between favorite and least favorite team trials. More importantly, functional connectivities of this cluster with the human reward system were specifically involved in the type of WT investigated in our study, thus supporting the idea of an attention bias generating WT. Prefrontal cortex activity also distinguished between the two teams. However, activity in this region and its functional connectivities with the human reward system were altogether unrelated to the degree of WT reflected in the participants’ behavior and may rather be related to social identification, ensuring the affective context necessary for WT to arise. PMID:22198967

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

PubMed

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

PubMed Central

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context. PMID:20113515

A Novel Persistence Associated EBV miRNA Expression Profile Is Disrupted in Neoplasia

PubMed Central

Qiu, Jin; Cosmopoulos, Katherine; Pegtel, Michiel; Hopmans, Erik; Murray, Paul; Middeldorp, Jaap; Shapiro, Michael; Thorley-Lawson, David A.

2011-01-01

We have performed the first extensive profiling of Epstein-Barr virus (EBV) miRNAs on in vivo derived normal and neoplastic infected tissues. We describe a unique pattern of viral miRNA expression by normal infected cells in vivo expressing restricted viral latency programs (germinal center: Latency II and memory B: Latency I/0). This includes the complete absence of 15 of the 34 miRNAs profiled. These consist of 12 BART miRNAs (including approximately half of Cluster 2) and 3 of the 4 BHRF1 miRNAs. All but 2 of these absent miRNAs become expressed during EBV driven growth (Latency III). Furthermore, EBV driven growth is accompanied by a 5–10 fold down regulation in the level of the BART miRNAs expressed in germinal center and memory B cells. Therefore, Latency III also expresses a unique pattern of viral miRNAs. We refer to the miRNAs that are specifically expressed in EBV driven growth as the Latency III associated miRNAs. In EBV associated tumors that employ Latency I or II (Burkitt's lymphoma, Hodgkin's disease, nasopharyngeal carcinoma and gastric carcinoma), the Latency III associated BART but not BHRF1 miRNAs are up regulated. Thus BART miRNA expression is deregulated in the EBV associated tumors. This is the first demonstration that Latency III specific genes (the Latency III associated BARTs) can be expressed in these tumors. The EBV associated tumors demonstrate very similar patterns of miRNA expression yet were readily distinguished when the expression data were analyzed either by heat-map/clustering or principal component analysis. Systematic analysis revealed that the information distinguishing the tumor types was redundant and distributed across all the miRNAs. This resembles “secret sharing” algorithms where information can be distributed among a large number of recipients in such a way that any combination of a small number of recipients is able to understand the message. Biologically, this may be a consequence of functional redundancy between the miRNAs. PMID:21901094

Whole genome sequencing as a tool to investigate a cluster of seven cases of listeriosis in Austria and Germany, 2011-2013.

PubMed

Schmid, D; Allerberger, F; Huhulescu, S; Pietzka, A; Amar, C; Kleta, S; Prager, R; Preußel, K; Aichinger, E; Mellmann, A

2014-05-01

A cluster of seven human cases of listeriosis occurred in Austria and in Germany between April 2011 and July 2013. The Listeria monocytogenes serovar (SV) 1/2b isolates shared pulsed-field gel electrophoresis (PFGE) and fluorescent amplified fragment length polymorphism (fAFLP) patterns indistinguishable from those from five food producers. The seven human isolates, a control strain with a different PFGE/fAFLP profile and ten food isolates were subjected to whole genome sequencing (WGS) in a blinded fashion. A gene-by-gene comparison (multilocus sequence typing (MLST)+) was performed, and the resulting whole genome allelic profiles were compared using SeqSphere(+) software version 1.0. On analysis of 2298 genes, the four human outbreak isolates from 2012 to 2013 had different alleles at ≤6 genes, i.e. differed by ≤6 genes from each other; the dendrogram placed these isolates in between five Austrian unaged soft cheese isolates from producer A (≤19-gene difference from the human cluster) and two Austrian ready-to-eat meat isolates from producer B (≤8-gene difference from the human cluster). Both food products appeared on grocery bills prospectively collected by these outbreak cases after hospital discharge. Epidemiological results on food consumption and MLST+ clearly separated the three cases in 2011 from the four 2012-2013 outbreak cases (≥48 different genes). We showed that WGS is capable of discriminating L. monocytogenes SV1/2b clones not distinguishable by PFGE and fAFLP. The listeriosis outbreak described clearly underlines the potential of sequence-based typing methods to offer enhanced resolution and comparability of typing systems for public health applications. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

Application of Time-Dependent Density Functional and Natural Bond Orbital Theories to the UV-vis Absorption Spectra of Some Phenolic Compounds.

PubMed

Marković, Svetlana; Tošović, Jelena

2015-09-03

The UV-vis properties of 22 natural phenolic compounds, comprising anthraquinones, neoflavonoids, and flavonoids were systematically examined. The time-dependent density functional theory (TDDFT) approach in combination with the B3LYP, B3LYP-D2, B3P86, and M06-2X functionals was used to simulate the UV-vis spectra of the investigated compounds. It was shown that all methods exhibit very good (B3LYP slightly better) performance in reproducing the examined UV-vis spectra. However, the shapes of the Kohn-Sham molecular orbitals (MOs) involved in electronic transitions were misleading in constructing the MO correlation diagrams. To provide better understanding of redistribution of electron density upon excitation, the natural bond orbital (NBO) analysis was applied. Bearing in mind the spatial and energetic separations, as well as the character of the π bonding, lone pair, and π* antibonding natural localized molecular orbitals (NLMOs), the "NLMO clusters" were constructed. NLMO cluster should be understood as a part of a molecule characterized with distinguished electron density. It was shown that all absorption bands including all electronic transitions need to be inspected to fully understand the UV-vis spectrum of a certain compound, and, thus, to learn more about its UV-vis light absorption. Our investigation showed that the TDDFT and NBO theories are complementary, as the results from the two approaches can be combined to interpret the UV-vis spectra. Agreement between the predictions of the TDDFT approach and those based on the NLMO clusters is excellent in the case of major electronic transitions and long wavelengths. It should be emphasized that the approach for investigation of UV-vis light absorption based on the NLMO clusters is applied for the first time.

The dynamics of methane emissions in Alaskan peatlands at different trophic levels

NASA Astrophysics Data System (ADS)

Zhang, L.; Liu, X.; Langford, L.; Chanton, J.; Hines, M. E.

2016-12-01

One major uncertainty in estimating methane (CH4) emission from wetlands is extrapolating from highly heterogeneous and inadequately studied local sites to larger scales. The heterogeneity of peatlands comes from contrasting surface vegetation compositions within short distances that are usually associated with different nutrient sources and trophic status. Different microbial communities and metabolic pathways occur at different trophic levels. Stable isotope C ratios (δ13C) have been used as a robust tool to distinguish methanogenic pathways, but different sources of parent compounds (acetate and CO2) with unique δ13C signatures, and unresolved fractionation factors associated with different methanogens, add complexity. To better understand the relationships between trophic status, surface vegetation compositions and methanogenic pathways, 28 peatland sites were studied in Fairbanks and Anchorage, Alaska in the summer of 2015. These sites were ordinated using multiple factor analysis into 3 clusters based on pH, temp, CH4 and volatile fatty acids production rates, δ13C values, and surface vegetation composition. In the low-pH trophic cluster (pH 4.2), Sphagnum fuscum was the dominant species with specific sedges (Ledum decumbens), and primary fermentation rates was slow with no CH4 detected. In the intermediate trophic level (pH 5.3), in which Sphagnum magellanicum was largely present, both hydrogenotrophic (HM) and acetoclastic methanogenesis (AM) were very active. Syntrophy was present at certain sites, which may provide CO2 and acetate with unique δ13C for CH4 production. At the highest pH trophic cluster examined in this study (pH 5.8), Carex tenuiflora, Carex aquatilis, and Sphagnum Squarrosum dominated. CH4 production rates were higher than those in the intermediate cluster and the apparent fractionation factor a was lower.

Characteristic Metabolism of Free Amino Acids in Cetacean Plasma: Cluster Analysis and Comparison with Mice

PubMed Central

Miyaji, Kazuki; Nagao, Kenji; Bannai, Makoto; Asakawa, Hiroshi; Kohyama, Kaoru; Ohtsu, Dai; Terasawa, Fumio; Ito, Shu; Iwao, Hajime; Ohtani, Nobuyo; Ohta, Mitsuaki

2010-01-01

From an evolutionary perspective, the ancestors of cetaceans first lived in terrestrial environments prior to adapting to aquatic environments. Whereas anatomical and morphological adaptations to aquatic environments have been well studied, few studies have focused on physiological changes. We focused on plasma amino acid concentrations (aminograms) since they show distinct patterns under various physiological conditions. Plasma and urine aminograms were obtained from bottlenose dolphins, pacific white-sided dolphins, Risso's dolphins, false-killer whales and C57BL/6J and ICR mice. Hierarchical cluster analyses were employed to uncover a multitude of amino acid relationships among different species, which can help us understand the complex interrelations comprising metabolic adaptations. The cetacean aminograms formed a cluster that was markedly distinguishable from the mouse cluster, indicating that cetaceans and terrestrial mammals have quite different metabolic machinery for amino acids. Levels of carnosine and 3-methylhistidine, both of which are antioxidants, were substantially higher in cetaceans. Urea was markedly elevated in cetaceans, whereas the level of urea cycle-related amino acids was lower. Because diving mammals must cope with high rates of reactive oxygen species generation due to alterations in apnea/reoxygenation and ischemia-reperfusion processes, high concentrations of antioxidative amino acids are advantageous. Moreover, shifting the set point of urea cycle may be an adaption used for body water conservation in the hyperosmotic sea water environment, because urea functions as a major blood osmolyte. Furthermore, since dolphins are kept in many aquariums for observation, the evaluation of these aminograms may provide useful diagnostic indices for the assessment of cetacean health in artificial environments in the future. PMID:21072195

Consensus-Based Sorting of Neuronal Spike Waveforms

PubMed Central

Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

Nature's polyoxometalate chemistry: X-ray structure of the Mo storage protein loaded with discrete polynuclear Mo-O clusters.

PubMed

Kowalewski, Björn; Poppe, Juliane; Demmer, Ulrike; Warkentin, Eberhard; Dierks, Thomas; Ermler, Ulrich; Schneider, Klaus

2012-06-13

Some N(2)-fixing bacteria prolong the functionality of nitrogenase in molybdenum starvation by a special Mo storage protein (MoSto) that can store more than 100 Mo atoms. The presented 1.6 Å X-ray structure of MoSto from Azotobacter vinelandii reveals various discrete polyoxomolybdate clusters, three covalently and three noncovalently bound Mo(8), three Mo(5-7), and one Mo(3) clusters, and several low occupied, so far undefinable clusters, which are embedded in specific pockets inside a locked cage-shaped (αβ)(3) protein complex. The structurally identical Mo(8) clusters (three layers of two, four, and two MoO(n) octahedra) are distinguishable from the [Mo(8)O(26)](4-) cluster formed in acidic solutions by two displaced MoO(n) octahedra implicating three kinetically labile terminal ligands. Stabilization in the covalent Mo(8) cluster is achieved by Mo bonding to Hisα156-N(ε2) and Gluα129-O(ε1). The absence of covalent protein interactions in the noncovalent Mo(8) cluster is compensated by a more extended hydrogen-bond network involving three pronounced histidines. One displaced MoO(n) octahedron might serve as nucleation site for an inhomogeneous Mo(5-7) cluster largely surrounded by bulk solvent. In the Mo(3) cluster located on the 3-fold axis, the three accurately positioned His140-N(ε2) atoms of the α subunits coordinate to the Mo atoms. The formed polyoxomolybdate clusters of MoSto, not detectable in bulk solvent, are the result of an interplay between self- and protein-driven assembly processes that unite inorganic supramolecular and protein chemistry in a host-guest system. Template, nucleation/protection, and catalyst functions of the polypeptide as well as perspectives for designing new clusters are discussed.

Consensus-Based Sorting of Neuronal Spike Waveforms.

PubMed

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

[Food-related lifestyles and eating habits inside and outside the home in the Metropolitan Region of Santiago, Chile].

PubMed

Schnettler, Berta; Peña, Juan Pablo; Mora, Marcos; Miranda, Horacio; Sepúlveda, José; Denegri, Marianela; Lobos, Germán

2013-01-01

To distinguish consumer typologies on the basis of their food-related lifestyle in the principal municipalities of the Metropolitan Region of Santiago, Chile, and to characterize these according to their food consumption habits inside and outside the home, sociodemographic characteristics and their level of satisfaction with food-related life. A structured questionnaire was administered to a sample of 951 people in the principal municipalities of the Metropolitan Region of Santiago (more than 100,000 inhabitants). The instrument for collecting data included an adaptation of the food-related life (FRL) questionnaire and the satisfaction with food-related life (SWFL) scale. The food consumption habits inside and outside the home were asked about as well as sociodemographic classification variables of those surveyed. Using a cluster analysis, five typologies were distinguished with significant differences in the five components obtained from the FRL with a factorial analysis of the principal components. The typologies presented a different gender, age and socioeconomic level profile and differed in the scores obtained on the SWFL. They differed in the frequency with which the person has lunch, tea ("once" in Chile) and dinner at home. With respect to the meals outside the home, the typologies were distinguished according to the frequency of meals in restaurants, fast food outlets and in the purchase of prepared food. A lifestyle where eating is related to low involvement and enjoyment of food is associated with a person's higher socioeconomic level and lower age. Additionally, a greater frequency of meals in restaurants and the purchase of prepared food combined with a lower frequency of meals at home is associated with unhealthy eating habits of little benefit to the person, which might have a bearing on a lower level of food-related life satisfaction. Copyright © AULA MEDICA EDICIONES 2013. Published by AULA MEDICA. All rights reserved.

Electrocortical activity distinguishes between uphill and level walking in humans.

PubMed

Bradford, J Cortney; Lukos, Jamie R; Ferris, Daniel P

2016-02-01

The objective of this study was to determine if electrocortical activity is different between walking on an incline compared with level surface. Subjects walked on a treadmill at 0% and 15% grades for 30 min while we recorded electroencephalography (EEG). We used independent component (IC) analysis to parse EEG signals into maximally independent sources and then computed dipole estimations for each IC. We clustered cortical source ICs and analyzed event-related spectral perturbations synchronized to gait events. Theta power fluctuated across the gait cycle for both conditions, but was greater during incline walking in the anterior cingulate, sensorimotor and posterior parietal clusters. We found greater gamma power during level walking in the left sensorimotor and anterior cingulate clusters. We also found distinct alpha and beta fluctuations, depending on the phase of the gait cycle for the left and right sensorimotor cortices, indicating cortical lateralization for both walking conditions. We validated the results by isolating movement artifact. We found that the frequency activation patterns of the artifact were different than the actual EEG data, providing evidence that the differences between walking conditions were cortically driven rather than a residual artifact of the experiment. These findings suggest that the locomotor pattern adjustments necessary to walk on an incline compared with level surface may require supraspinal input, especially from the left sensorimotor cortex, anterior cingulate, and posterior parietal areas. These results are a promising step toward the use of EEG as a feed-forward control signal for ambulatory brain-computer interface technologies.

Olive oil or lard?: distinguishing plant oils from animal fats in the archeological record of the eastern Mediterranean using gas chromatography/combustion/isotope ratio mass spectrometry.

PubMed

Steele, Valerie J; Stern, Ben; Stott, Andy W

2010-12-15

Distinguishing animal fats from plant oils in archaeological residues is not straightforward. Characteristic plant sterols, such as β-sitosterol, are often missing in archaeological samples and specific biomarkers do not exist for most plant fats. Identification is usually based on a range of characteristics such as fatty acid ratios, all of which indicate that a plant oil may be present, none of which uniquely distinguish plant oils from other fats. Degradation and dissolution during burial alter fatty acid ratios and remove short-chain fatty acids, resulting in degraded plant oils with similar fatty acid profiles to other degraded fats. Compound-specific stable isotope analysis of δ(13)C(18:0) and δ(13)C(16:0), carried out by gas chromatography/combustion/isotope ratio mass spectrometry (GC/C/IRMS), has provided a means of distinguishing fish oils, dairy fats, ruminant and non-ruminant adipose fats, but plant oils are rarely included in these analyses. For modern plant oils where C(18:1) is abundant, δ(13)C(18:1) and δ(13)C(16:0) are usually measured. These results cannot be compared with archaeological data or data from other modern reference fats where δ(13)C(18:0) and δ(13)C(16:0) are measured, as C(18:0) and C(18:1) are formed by different processes resulting in different isotopic values. Eight samples of six modern plant oils were saponified, releasing sufficient C(18:0) to measure the isotopic values, which were plotted against δ(13)C(16:0). The isotopic values for these oils, with one exception, formed a tight cluster between ruminant and non-ruminant animal fats. This result complicates the interpretation of mixed fatty residues in geographical areas where both animal fats and plant oils were in use. Copyright © 2010 John Wiley & Sons, Ltd.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy.

PubMed

Liu, Zhenli; Liu, Yuanyan; Liu, Chunsheng; Song, Zhiqian; Li, Qing; Zha, Qinglin; Lu, Cheng; Wang, Chun; Ning, Zhangchi; Zhang, Yuxin; Tian, Cheng; Lu, Aiping

2013-07-12

Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. The established chemotaxonomic classification could be effectively used for Rhodiola species identification.

Differential Adjustment Among Rural Adolescents Exposed to Family Violence

PubMed Central

Sianko, Natallia; Hedge, Jasmine M.; McDonell, James R.

2016-01-01

This study examines differences in psychological adjustment in a sample of rural adolescents who have been exposed to family violence. Self-report questionnaires were administered to 580 adolescents and their primary caregivers. The results revealed that over two thirds of the study participants (68.8%) had been exposed to violence in their families. As hypothesized, cluster analysis identified several profiles among adolescents, distinguished by their psychological and emotional functioning: well adjusted (46.2%), moderately adjusted (44.3%), and struggling (9.5%). Discriminant function analysis confirmed the groupings and revealed that family functioning was among the most influential factors explaining adjustment differences. Multivariate analyses of variance (MANOVAs) further showed that adolescents from each of the three adjustment profiles reported significantly different levels of family social support, parental involvement, and perceived neighborhood safety. Overall, the results confirm heterogeneity of adolescent adaptation in the aftermath of family violence and provide insights into family and neighborhood factors that account for variability in adolescents’ reactions to violence. Implications for future research and practical interventions are discussed. PMID:27106255

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy

PubMed Central

2013-01-01

Background Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. Results In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. Conclusions The established chemotaxonomic classification could be effectively used for Rhodiola species identification. PMID:23844866

Accurate determination of genetic identity for a single cacao bean, using molecular markers with a nanofluidic system, ensures cocoa authentication.

PubMed

Fang, Wanping; Meinhardt, Lyndel W; Mischke, Sue; Bellato, Cláudia M; Motilal, Lambert; Zhang, Dapeng

2014-01-15

Cacao (Theobroma cacao L.), the source of cocoa, is an economically important tropical crop. One problem with the premium cacao market is contamination with off-types adulterating raw premium material. Accurate determination of the genetic identity of single cacao beans is essential for ensuring cocoa authentication. Using nanofluidic single nucleotide polymorphism (SNP) genotyping with 48 SNP markers, we generated SNP fingerprints for small quantities of DNA extracted from the seed coat of single cacao beans. On the basis of the SNP profiles, we identified an assumed adulterant variety, which was unambiguously distinguished from the authentic beans by multilocus matching. Assignment tests based on both Bayesian clustering analysis and allele frequency clearly separated all 30 authentic samples from the non-authentic samples. Distance-based principle coordinate analysis further supported these results. The nanofluidic SNP protocol, together with forensic statistical tools, is sufficiently robust to establish authentication and to verify gourmet cacao varieties. This method shows significant potential for practical application.

Feedback in the Antennae Galaxies (NGC 4038/9): I. High-Resolution Infrared Spectroscopy of Winds from Super Star Clusters

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

Gilbert, A; Graham, J

2007-06-05

We present high-resolution (R {approx} 24,600) near-IR spectroscopy of the youngest super star clusters (SSCs) in the prototypical starburst merger, the Antennae Galaxies. These SSCs are young (3-7 Myr old) and massive (10{sup 5}-10{sup 7} M{sub {circle_dot}} for a Kroupa IMF) and their spectra are characterized by broad, extended Brackett {gamma} emission, so we refer to them as emission-line clusters (ELCs) to distinguish them from older SSCs. The Br {gamma} lines of most ELCs have supersonic widths (60-110 km s{sup -1} FWHM) and non-Gaussian wings whose velocities exceed the clusters escape velocities. This high-velocity unbound gas is flowing out inmore » winds that are powered by the clusters massive O and W-R stars over the course of at least several crossing times. The large sizes of some ELCs relative to those of older SSCs may be due to expansion caused by these outflows; many of the ELCs may not survive as bound stellar systems, but rather dissipate rapidly into the field population. The observed tendency of older ELCs to be more compact than young ones is consistent with the preferential survival of the most concentrated clusters at a given age.« less

Unique glycosignature for intervertebral disc and articular cartilage cells and tissues in immaturity and maturity.

PubMed

Collin, E C; Kilcoyne, M; White, S J; Grad, S; Alini, M; Joshi, L; Pandit, A S

2016-03-11

In this study, on/off markers for intervertebral disc (IVD) and articular cartilage (AC) cells (chondrocytes) and distinct glycoprofiles of cell and tissue-types were identified from immaturity to maturity. Three and eleven month-old ovine IVD and AC tissues were histochemically profiled with a panel of lectins and antibodies. Relationships between tissue and cell types were analysed by hierarchical clustering. Chondroitin sulfate (CS) composition of annulus fibrosus (AF), nucleus pulposus (NP) and AC tissues was determined by HPLC analysis. Clear on/off cell type markers were identified, which enabled the discrimination of chondrocytes, AF and NP cells. AF and NP cells were distinguishable using MAA, SNA-I, SBA and WFA lectins, which bound to both NP cells and chondrocytes but not AF cells. Chondrocytes were distinguished from NP and AF cells with a specific binding of LTA and PNA lectins to chondrocytes. Each tissue showed a unique CS composition with a distinct switch in sulfation pattern in AF and NP tissues upon disc maturity while cartilage maintained the same sulfation pattern over time. In conclusion, distinct glycoprofiles for cell and tissue-types across age groups were identified in addition to altered CS composition and sulfation patterns for tissue types upon maturity.

Species identification of mutans streptococci by groESL gene sequence.

PubMed

Hung, Wei-Chung; Tsai, Jui-Chang; Hsueh, Po-Ren; Chia, Jean-San; Teng, Lee-Jene

2005-09-01

The near full-length sequences of the groESL genes were determined and analysed among eight reference strains (serotypes a to h) representing five species of mutans group streptococci. The groES sequences from these reference strains revealed that there are two lengths (285 and 288 bp) in the five species. The intergenic spacer between groES and groEL appears to be a unique marker for species, with a variable size (ranging from 111 to 310 bp) and sequence. Phylogenetic analysis of groES and groEL separated the eight serotypes into two major clusters. Strains of serotypes b, c, e and f were highly related and had groES gene sequences of the same length, 288 bp, while strains of serotypes a, d, g and h were also closely related and their groES gene sequence lengths were 285 bp. The groESL sequences in clinical isolates of three serotypes of S. mutans were analysed for intraspecies polymorphism. The results showed that the groESL sequences could provide information for differentiation among species, but were unable to distinguish serotypes of the same species. Based on the determined sequences, a PCR assay was developed that could differentiate members of the mutans streptococci by amplicon size and provide an alternative way for distinguishing mutans streptococci from other viridans streptococci.

An initial perspective of S-asteroid subtypes within asteroid families

NASA Technical Reports Server (NTRS)

Kelley, M. S.; Gaffey, M. J.

1993-01-01

Many main belt asteroids cluster around certain values of semi-major axis (a), inclination (i), and eccentricity (e). Hirayama was the first to notice these concentrations which he interpreted as evidence of disruptions of larger parent bodies. He called these clusters 'asteroid families'. The term 'families' is increasingly reserved for genetic associations to distinguish them from clusters of unknown or purely dynamical origin (e.g. the Phocaea cluster). Members of a genetic asteroid family represent fragments derived from various depths within the original parent planetesimal. Thus, family members offer the potential for direct examination of the interiors of parent bodies which have undergone metamorphism and differentiation similar to that occurring in the inaccessible interiors of terrestrial planets. The differentiation similar to that occurring in the inaccessible interiors of terrestrial planets. The condition that genetic family members represent the fragments of a parent object provides a critical test of whether an association (cluster in proper element space) is a genetic family. Compositions (types and relative abundances of materials) of family members must permit the reconstruction of a compositionally plausible parent body. The compositions of proposed family members can be utilized to test the genetic reality of the family and to determine the type and degree of internal differentiation within the parent planetesimal. The interpretation of the S-class mineralogy provides a preliminary evaluation of family memberships. Detailed mineralogical and petrological analysis was done based on the reflectance spectra of 39 S-type asteroids. The result is a division of the S-asteroid class into seven subtypes based on compositional differences. These subtypes, designated S(I) to S(VII), correspond to surface silicate assemblages ranging from monomineralic olivine (dunites) through olivine-pyroxene mixtures to pure pyroxene or pyroxene-feldspar mixtures (basalts). The most general conclusion is that the S-asteroids cannot be treated as a single group of objects without greatly oversimplifying their properties. Each S-subtype needs to be treated as an independent group with a distinct evolutionary history.

Light absorption, optical and microphysical properties of trajectory-clustered aerosols at two AERONET sites in West Africa

NASA Astrophysics Data System (ADS)

Fawole, O. G.; Cai, X.; MacKenzie, A. R.

2015-12-01

Aerosol remote sensing techniques and back-trajectory modeling can be combined to identify aerosol types. We have clustered 7 years of AERONET aerosol signals using trajectory analysis to identify dominant aerosol sources at two AERONET sites in West Africa: Ilorin (4.34 oE, 8.32 oN) and Djougou (1.60 oE, 9.76 oN). Of particular interest are air masses that have passed through the gas flaring region in the Niger Delta area, of Nigeria, en-route the AERONET sites. 7-day back trajectories were calculated using the UK UGAMP trajectory model driven by ECMWF wind analyses data. Dominant sources identified, using literature classifications, are desert dust (DD), Biomass burning (BB) and Urban-Industrial (UI). Below, we use a combination of synoptic trajectories and aerosol optical properties to distinguish a fourth source: that due to gas flaring. Gas flaring, (GF) the disposal of gas through stack in an open-air flame, is believed to be a prominent source of black carbon (BC) and greenhouse gases. For these different aerosol source signatures, single scattering albedo (SSA), refractive index , extinction Angstrom exponent (EEA) and absorption Angstrom exponent (AAE) were used to classify the light absorption characteristics of the aerosols for λ = 440, 675, 870 and1020 nm. A total of 1625 daily averages of aerosol data were collected for the two sites. Of which 245 make up the GF cluster for both sites. For GF cluster, the range of fine-mode fraction is 0.4 - 0.7. Average values SSA(λ), for the total and GF clusters are 0.90(440), 0.93(675), 0.95(870) and 0.96(1020), and 0.93(440), 0.92(675), 0.9(870) and 0.9(1020), respectively. Values of for the GF clusters for both sites are 0.62 - 1.11, compared to 1.28 - 1.66 for the remainder of the clusters, which strongly indicates the dominance of carbonaceous particles (BC), typical of a highly industrial area. An average value of 1.58 for the real part of the refractive index at low SSA for aerosol in the GF cluster is also an indicator of high BC content. Extinction Angstrom exponent, is an indicator of the particle size. EAE values of 0.95-1.32 for aerosol in the GF cluster shows that the aerosols are mainly fine or accumulation mode while values of EAE (0.36-0.6) for the other cluster indicate coarse mode domination of the aerosol. See table 1 for a summary of result.

Petrofacies Analysis - A Petrophysical Tool for Geologic/Engineering Reservoir Characterization

USGS Publications Warehouse

Watney, W.L.; Guy, W.J.; Doveton, J.H.; Bhattacharya, S.; Gerlach, P.M.; Bohling, Geoffrey C.; Carr, T.R.

1998-01-01

Petrofacies analysis is defined as the characterization and classification of pore types and fluid saturations as revealed by petrophysical measurements of a reservoir. The word "petrofacies" makes an explicit link between petroleum engineers' concerns with pore characteristics as arbiters of production performance and the facies paradigm of geologists as a methodology for genetic understanding and prediction. In petrofacies analysis, the porosity and resistivity axes of the classical Pickett plot are used to map water saturation, bulk volume water, and estimated permeability, as well as capillary pressure information where it is available. When data points are connected in order of depth within a reservoir, the characteristic patterns reflect reservoir rock character and its interplay with the hydrocarbon column. A third variable can be presented at each point on the crossplot by assigning a color scale that is based on other well logs, often gamma ray or photoelectric effect, or other derived variables. Contrasts between reservoir pore types and fluid saturations are reflected in changing patterns on the crossplot and can help discriminate and characterize reservoir heterogeneity. Many hundreds of analyses of well logs facilitated by spreadsheet and object-oriented programming have provided the means to distinguish patterns typical of certain complex pore types (size and connectedness) for sandstones and carbonate reservoirs, occurrences of irreducible water saturation, and presence of transition zones. The result has been an improved means to evaluate potential production, such as bypassed pay behind pipe and in old exploration wells, or to assess zonation and continuity of the reservoir. Petrofacies analysis in this study was applied to distinguishing flow units and including discriminating pore type as an assessment of reservoir conformance and continuity. The analysis is facilitated through the use of colorimage cross sections depicting depositional sequences, natural gamma ray, porosity, and permeability. Also, cluster analysis was applied to discriminate petrophysically similar reservoir rock.

On Defect Cluster Aggregation and Non-Reducibilty in Tin-Doped Indium Oxide

NASA Astrophysics Data System (ADS)

Warschkow, Oliver; Ellis, Donald E.; Gonzalez, Gabriela; Mason, Thomas O.

2003-03-01

The conductivity of tin-doped indium oxide (ITO), a transparent conductor, is critically dependent on the amount of tin-doping and oxygen partial pressure during preparation and annealing. Frank and Kostlin (Appl. Phys. A 27 (1982) 197-206) rationalized the carrier concentration dependence by postulating the formation of two types of neutral defect clusters at medium tin-doping levels: "Reducible" and "non-reducible" defect clusters; so named to indicate their ability to create carriers under reduction. According to Frank and Kostlin, both are composed of a single oxygen interstitial and two tin atoms substituting for indium, positioned in non-nearest and nearest coordination, respectively. This present work, seeking to distinguish reducible and non-reducible clusters by use of an atomistic model, finds only a weak correlation of oxygen interstitial binding energies with the relative positioning of dopants. Instead, the number of tin-dopants in the vicinity of the interstitial has a much larger effect on how strongly it is bound, a simple consequence of Coulomb interactions. We postulate that oxygen interstitials become non-reducible when clustered with three or more Sn_In. This occurs at higher doping levels as reducible clusters aggregate and share tin atoms. A simple probabilistic model, estimating the average number of clusters so aggregated, provides a qualitatively correct description of the carrier density in reduced ITO as a function of Sn doping level.

Thermal and log-normal distributions of plasma in laser driven Coulomb explosions of deuterium clusters

NASA Astrophysics Data System (ADS)

Barbarino, M.; Warrens, M.; Bonasera, A.; Lattuada, D.; Bang, W.; Quevedo, H. J.; Consoli, F.; de Angelis, R.; Andreoli, P.; Kimura, S.; Dyer, G.; Bernstein, A. C.; Hagel, K.; Barbui, M.; Schmidt, K.; Gaul, E.; Donovan, M. E.; Natowitz, J. B.; Ditmire, T.

2016-08-01

In this work, we explore the possibility that the motion of the deuterium ions emitted from Coulomb cluster explosions is highly disordered enough to resemble thermalization. We analyze the process of nuclear fusion reactions driven by laser-cluster interactions in experiments conducted at the Texas Petawatt laser facility using a mixture of D2+3He and CD4+3He cluster targets. When clusters explode by Coulomb repulsion, the emission of the energetic ions is “nearly” isotropic. In the framework of cluster Coulomb explosions, we analyze the energy distributions of the ions using a Maxwell-Boltzmann (MB) distribution, a shifted MB distribution (sMB), and the energy distribution derived from a log-normal (LN) size distribution of clusters. We show that the first two distributions reproduce well the experimentally measured ion energy distributions and the number of fusions from d-d and d-3He reactions. The LN distribution is a good representation of the ion kinetic energy distribution well up to high momenta where the noise becomes dominant, but overestimates both the neutron and the proton yields. If the parameters of the LN distributions are chosen to reproduce the fusion yields correctly, the experimentally measured high energy ion spectrum is not well represented. We conclude that the ion kinetic energy distribution is highly disordered and practically not distinguishable from a thermalized one.

Investigating a tuberculosis cluster among Filipino health care workers in a low-incidence country.

PubMed

Davidson, J A; Fulton, N; Thomas, H L; Lalor, M K; Zenner, D; Brown, T; Murphy, S; Anderson, L F

2018-03-01

Nearly 8% of adult tuberculosis (TB) cases in England, Wales and Northern Ireland (EW&NI) occur among health care workers (HCWs), the majority of whom are from high TB incidence countries. To determine if a TB cluster containing multiple HCWs was due to nosocomial transmission. A cluster of TB cases notified in EW&NI from 2009 to 2014, with indistinguishable 24-locus mycobacterial interspersed repetitive unit-variable number of tandem repeats (MIRU-VNTR) profiles, was identified through routine national cluster review. Cases were investigated to identify epidemiological links, and occupational health (OH) information was collected for HCW cases. To further discriminate strains, typing of eight additional loci was conducted. Of the 53 cases identified, 22 were HCWs. The majority (n = 43), including 21 HCWs, were born in the Philippines. Additional typing split the cluster into three subclusters and seven unique strains. No epidemiological links were identified beyond one household and a common residential area. HCWs in this cluster received no or inadequate OH assessment. The MIRU-VNTR profile of this cluster probably reflects common endemic strains circulating in the Philippines, with reactivation occurring in the UK. Furthermore, 32-locus typing showed that 24-locus MIRU-VNTR failed to distinguish strain diversity. The lack of OH assessment indicates that latent tuberculous infection could have been identified and treated, thereby preventing active cases from occurring.

His86 from the N-terminus of frataxin coordinates iron and is required for Fe-S cluster synthesis.

PubMed

Gentry, Leslie E; Thacker, Matthew A; Doughty, Reece; Timkovich, Russell; Busenlehner, Laura S

2013-09-03

Human frataxin has a vital role in the biosynthesis of iron-sulfur (Fe-S) clusters in mitochondria, and its deficiency causes the neurodegenerative disease Friedreich's ataxia. Proposed functions for frataxin in the Fe-S pathway include iron donation to the Fe-S cluster machinery and regulation of cysteine desulfurase activity to control the rate of Fe-S production, although further molecular detail is required to distinguish these two possibilities. It is well established that frataxin can coordinate iron using glutamate and aspartate side chains on the protein surface; however, in this work we identify a new iron coordinating residue in the N-terminus of human frataxin using complementary spectroscopic and structural approaches. Further, we demonstrate that His86 in this N-terminal region is required for high affinity iron coordination and iron assembly of Fe-S clusters by ISCU as part of the Fe-S cluster biosynthetic complex. If a binding site that includes His86 is important for Fe-S cluster synthesis as part of its chaperone function, this raises the possibility that either iron binding at the acidic surface of frataxin may be spurious or that it is required for protein-protein interactions with the Fe-S biosynthetic quaternary complex. Our data suggest that iron coordination to frataxin may be significant to the Fe-S cluster biosynthesis pathway in mitochondria.

Use of joint two-view information for computerized lesion detection on mammograms: improvement of microcalcification detection accuracy

NASA Astrophysics Data System (ADS)

Sahiner, Berkman; Gurcan, Metin N.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

2002-05-01

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Object pairs were classified with a joint two-view classifier that used the similarity of objects in a pair. Each cluster candidate was also classified as a true microcalcification cluster or a false-positive (FP) using its single-view features. The outputs of these two classifiers were fused. A data set of 38 pairs of mammograms from our database was used to train the new detection technique. The independent test set consisted of 77 pairs of mammograms from the University of South Florida public database. At a per-film sensitivity of 70%, the FP rates were 0.17 and 0.27 with the fusion and single-view detection methods, respectively. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing false from true microcalcification clusters.

Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels

NASA Astrophysics Data System (ADS)

Moghaddasi, Hanieh; Khalifeh, Khosrow; Darooneh, Amir Hossein

2017-01-01

Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones.

HOW SIGNIFICANT IS RADIATION PRESSURE IN THE DYNAMICS OF THE GAS AROUND YOUNG STELLAR CLUSTERS?

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

Silich, Sergiy; Tenorio-Tagle, Guillermo, E-mail: silich@inaoep.mx

2013-03-01

The impact of radiation pressure on the dynamics of the gas in the vicinity of young stellar clusters is thoroughly discussed. The radiation over the thermal/ram pressure ratio time evolution is calculated explicitly and the crucial roles of the cluster mechanical power, the strong time evolution of the ionizing photon flux, and the bolometric luminosity of the exciting cluster are stressed. It is shown that radiation has only a narrow window of opportunity to dominate the wind-driven shell dynamics. This may occur only at early stages of the bubble evolution and if the shell expands into a dusty and/or amore » very dense proto-cluster medium. The impact of radiation pressure on the wind-driven shell always becomes negligible after about 3 Myr. Finally, the wind-driven model results allow one to compare the model predictions with the distribution of thermal pressure derived from X-ray observations. The shape of the thermal pressure profile then allows us to distinguish between the energy and the momentum-dominated regimes of expansion and thus conclude whether radiative losses of energy or the leakage of hot gas from the bubble interior have been significant during bubble evolution.« less

Comparison of 17 genome types of adenovirus type 3 identified among strains recovered from six continents.

PubMed Central

Li, Q G; Wadell, G

1988-01-01

Restriction endonucleases BamHI, BclI, BglI, BglII, BstEII, EcoRI, HindIII, HpaI, SalI, SmalI, XbalI, and XholI were used to analyze 61 selected strains of adenovirus type 3 (Ad3) isolated from Africa, Asia, Australia, Europe, North America, and South America. It was noted that the use of BamHI, BclI, BglII, HpaI, SalI, and SmaI was sufficient to distinguish 17 genome types; 13 of them were newly identified. All 17 Ad3 genome types could be divided into three genomic clusters. Genome types of Ad3 cluster 1 occurred in Africa, Europe, South America, and North America. Genomic cluster 2 was identified in Africa; genomic cluster 3 was identified in Africa, Asia, Australia, Europe (a few), and North America. This was of interest because 15 identified genome types of Ad7 could also be divided into three genomic clusters. The degree of genetic relatedness between the 17 Ad3 and the 15 Ad7 genome types was analyzed and was expressed in a three-dimensional model. Images PMID:2838500

Mechanisms behind overshoots in mean cluster size profiles in aggregation-breakup processes.

PubMed

Sadegh-Vaziri, Ramiar; Ludwig, Kristin; Sundmacher, Kai; Babler, Matthaus U

2018-05-26

Aggregation and breakup of small particles in stirred suspensions often shows an overshoot in the time evolution of the mean cluster size: Starting from a suspension of primary particles the mean cluster size first increases before going through a maximum beyond which a slow relaxation sets in. Such behavior was observed in various systems, including polymeric latices, inorganic colloids, asphaltenes, proteins, and, as shown by independent experiments in this work, in the flocculation of microalgae. This work aims at investigating possible mechanism to explain this phenomenon using detailed population balance modeling that incorporates refined rate models for aggregation and breakup of small particles in turbulence. Four mechanisms are considered: (1) restructuring, (2) decay of aggregate strength, (3) deposition of large clusters, and (4) primary particle aggregation where only aggregation events between clusters and primary particles are permitted. We show that all four mechanisms can lead to an overshoot in the mean size profile, while in contrast, aggregation and breakup alone lead to a monotonic, "S"-shaped size evolution profile. In order to distinguish between the different mechanisms simple protocols based on variations of the shear rate during the aggregation-breakup process are proposed. Copyright © 2018 Elsevier Inc. All rights reserved.

Metadata from data: identifying holidays from anesthesia data.

PubMed

Starnes, Joseph R; Wanderer, Jonathan P; Ehrenfeld, Jesse M

2015-05-01

The increasingly large databases available to researchers necessitate high-quality metadata that is not always available. We describe a method for generating this metadata independently. Cluster analysis and expectation-maximization were used to separate days into holidays/weekends and regular workdays using anesthesia data from Vanderbilt University Medical Center from 2004 to 2014. This classification was then used to describe differences between the two sets of days over time. We evaluated 3802 days and correctly categorized 3797 based on anesthesia case time (representing an error rate of 0.13%). Use of other metrics for categorization, such as billed anesthesia hours and number of anesthesia cases per day, led to similar results. Analysis of the two categories showed that surgical volume increased more quickly with time for non-holidays than holidays (p < 0.001). We were able to successfully generate metadata from data by distinguishing holidays based on anesthesia data. This data can then be used for economic analysis and scheduling purposes. It is possible that the method can be expanded to similar bimodal and multimodal variables.

Using Wavelet Analysis To Assist in Identification of Significant Events in Molecular Dynamics Simulations.

PubMed

Heidari, Zahra; Roe, Daniel R; Galindo-Murillo, Rodrigo; Ghasemi, Jahan B; Cheatham, Thomas E

2016-07-25

Long time scale molecular dynamics (MD) simulations of biological systems are becoming increasingly commonplace due to the availability of both large-scale computational resources and significant advances in the underlying simulation methodologies. Therefore, it is useful to investigate and develop data mining and analysis techniques to quickly and efficiently extract the biologically relevant information from the incredible amount of generated data. Wavelet analysis (WA) is a technique that can quickly reveal significant motions during an MD simulation. Here, the application of WA on well-converged long time scale (tens of μs) simulations of a DNA helix is described. We show how WA combined with a simple clustering method can be used to identify both the physical and temporal locations of events with significant motion in MD trajectories. We also show that WA can not only distinguish and quantify the locations and time scales of significant motions, but by changing the maximum time scale of WA a more complete characterization of these motions can be obtained. This allows motions of different time scales to be identified or ignored as desired.

Identification of RAPD and SCAR markers associated with yield traits in the Indian tropical tasar silkworm Antheraea mylitta drury

PubMed Central

Dutta, Suhrid R.; Kar, Prasanta K.; Srivastava, Ashok K.; Sinha, Manoj K.; Shankar, Jai; Ghosh, Ananta K.

2012-01-01

The tropical tasar silkworm, Antheraea mylitta, is a semi-domesticated vanya silk-producing insect of high economic importance. To date, no molecular marker associated with cocoon and shell weights has been identified in this species. In this report, we identified a randomly amplified polymorphic DNA (RAPD) marker and examined its inheritance, and also developed a stable diagnostic sequence-characterized amplified region (SCAR) marker. Silkworms were divided into groups with high (HCSW) and low (LCSW) cocoon and shell weights, and the F2 progeny of a cross between these two groups were obtained. DNA from these silkworms was screened by PCR using 34 random primers and the resulting RAPD fragments were used for cluster analysis and discriminant function analysis (DFA). The clustering pattern in a UPGMA-based dendogram and DFA clearly distinguished the HCSW and LCSW groups. Multiple regression analysis identified five markers associated with cocoon and shell weights. The marker OPW16905 bp showed the most significant association with cocoon and shell weights, and its inheritance was confirmed in F2 progeny. Cloning and sequencing of this 905 bp fragment showed 88% identity between its 134 nucleotides and the Bmc-1/Yamato-like retroposon of A. mylitta. This marker was further converted into a diagnostic SCAR marker (SCOPW 16826 bp). The SCAR marker developed here may be useful in identifying the right parental stock of tasar silk-worms for high cocoon and shell weights in breeding programs designed to enhance the productivity of tasar silk. PMID:23271934

Carbon, Hydrogen, and Oxygen Isotope Ratios of Cellulose from Plants Having Intermediary Photosynthetic Modes 1

PubMed Central

Sternberg, Leonel O'Reilly; Deniro, Michael J.; Ting, Irwin P.

1984-01-01

Carbon and hydrogen isotope ratios of cellulose nitrate and oxygen isotope ratios of cellulose from species of greenhouse plants having different photosynthetic modes were determined. When hydrogen isotope ratios are plotted against carbon isotope ratios, four clusters of points are discernible, each representing different photosynthetic modes: C3 plants, C4 plants, CAM plants, and C3 plants that can shift to CAM or show the phenomenon referred to as CAM-cycling. The combination of oxygen and carbon isotope ratios does not distinguish among the different photosynthetic modes. Analysis of the carbon and hydrogen isotope ratios of cellulose nitrate should prove useful for screening different photosynthetic modes in field specimens that grew near one another. This method will be particularly useful for detection of plants which show CAM-cycling. PMID:16663360

An intrinsic representation of atomic structure: From clusters to periodic systems

NASA Astrophysics Data System (ADS)

Li, Xiao-Tian; Xu, Shao-Gang; Yang, Xiao-Bao; Zhao, Yu-Jun

2017-10-01

We have improved our distance matrix and eigen-subspace projection function (EPF) [X.-T. Li et al., J. Chem. Phys. 146, 154108 (2017)] to describe the atomic structure for periodic systems. Depicting the local structure of an atom, the EPF turns out to be invariant with respect to the choices of the unit cell and coordinate frame, leading to an intrinsic representation of the crystal with a set of EPFs of the nontrivial atoms. The difference of EPFs reveals the difference of atoms in local structure, while the accumulated difference between two sets of EPFs can be taken as the distance between configurations. Exemplified with the cases of carbon allotropes and boron sheets, our EPF approach shows exceptional rationality and efficiency to distinguish the atomic structures, which is crucial in structure recognition, comparison, and analysis.

Environmental factors explaining the vegetation patterns in a temperate peatland.

PubMed

Pellerin, Stéphanie; Lagneau, Louis-Adrien; Lavoie, Martin; Larocque, Marie

2009-08-01

Although ombrotrophic temperate peatlands are important ecosystems for maintaining biodiversity in eastern North America, the environmental factors influencing their flora are only partly understood. The relationships between plant species distribution and environmental factors were thus studied within the oldest temperate peatland of Québec. Plant assemblages were identified by cluster analysis while CCA was used to related vegetation gradients to environmental factors. Five assemblages were identified; three typical of open bog and two characterized by more minerotrophic vegetation. Thicker peat deposit was encounter underlying the bog assemblages while higher water table level and percentage of free surface water distinguished the minerotrophic assemblages. Overall, the floristic patterns observed were spatially structured along the margins and the expanse. The most important environmental factors explaining this spatial gradient were the percentage of free surface water and the highest water-table level.

An intrinsic representation of atomic structure: From clusters to periodic systems.

PubMed

Li, Xiao-Tian; Xu, Shao-Gang; Yang, Xiao-Bao; Zhao, Yu-Jun

2017-10-14

We have improved our distance matrix and eigen-subspace projection function (EPF) [X.-T. Li et al., J. Chem. Phys. 146, 154108 (2017)] to describe the atomic structure for periodic systems. Depicting the local structure of an atom, the EPF turns out to be invariant with respect to the choices of the unit cell and coordinate frame, leading to an intrinsic representation of the crystal with a set of EPFs of the nontrivial atoms. The difference of EPFs reveals the difference of atoms in local structure, while the accumulated difference between two sets of EPFs can be taken as the distance between configurations. Exemplified with the cases of carbon allotropes and boron sheets, our EPF approach shows exceptional rationality and efficiency to distinguish the atomic structures, which is crucial in structure recognition, comparison, and analysis.

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

PubMed

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

2011-12-21

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

Geochemical prospecting for Cu mineralization in an arid terrain-central Iran

NASA Astrophysics Data System (ADS)

Mokhtari, Ahmad Reza; Roshani Rodsari, Parisa; Fatehi, Moslem; Shahrestani, Shahed; Pournik, Peyman

2014-12-01

Geochemical sampling and data processing were implemented for prospecting Cu mineralization through catchment basin approach in central Iran, Yazd province, over drainage systems in order to determine areas of interest for the detailed exploration program. The target zone, inside an area called Kalout-e-Ashrafa in Yazd province-Iran, was characterized by the collection of 107 stream sediment samples. Catchment basin modeling was conducted based on digital elevation model (DEM) and geological map of the study area. Samples were studied by univariate and multivariate statistical techniques of exploratory data analysis, classical statistical analysis and cluster analysis. The results showed that only Cu had anomalous behavior and it did not exhibit a considerable correlation with other elements. Geochemical maps were prepared for Cu and anomalous zones and separated for potential copper mineralization. It was concluded that due to especial geomorphological and geographical characteristics (smooth topography, negligible annual precipitation and insufficient thickness of silicified Cu-bearing outcrops of the area), low concentrations of Cu would be expected for the delineation of promising zones in similar trains. Using cluster analysis showed that there was a strong correlation between Ag, Sr and S. Calcium and Pb present moderate correlation with Cu. Additionally, there was a strong correlation between Zn and Li, thereby indicating a meaningful correlation with Fe, P, Ti and Mg. Aluminum, Sc and V had a correlation with Be and K. Applying threshold value according to MAD (median absolute deviation) helped us to distinguish anomalous catchments more properly. Finally, there was a significant kind of conformity among anomalous catchment basins and silicified veins and veinlets (as validating index) at the central part of the area.

Quality Evaluation of Juniperus rigida Sieb. et Zucc. Based on Phenolic Profiles, Bioactivity, and HPLC Fingerprint Combined with Chemometrics

PubMed Central

Liu, Zehua; Wang, Dongmei; Li, Dengwu; Zhang, Shuai

2017-01-01

Juniperus rigida (J. rigida) which is endemic to East Asia, has traditionally been used as an ethnomedicinal plant in China. This study was undertaken to evaluate the quality of J. rigida samples derived from 11 primary regions in China. Ten phenolic compounds were simultaneously quantified using reversed-phase high-performance liquid chromatography (RP-HPLC), and chlorogenic acid, catechin, podophyllotoxin, and amentoflavone were found to be the main compounds in J. rigida needles, with the highest contents detected for catechin and podophyllotoxin. J. rigida from Jilin (S9, S10) and Liaoning (S11) exhibited the highest contents of phenolic profiles (total phenolics, total flavonoids and 10 phenolic compounds) and the strongest antioxidant and antibacterial activities, followed by Shaanxi (S2, S3). A similarity analysis (SA) demonstrated substantial similarities in fingerprint chromatograms, from which 14 common peaks were selected. The similarity values varied from 0.85 to 0.98. Chemometrics techniques, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and discriminant analysis (DA), were further applied to facilitate accurate classification and quantification of the J. rigida samples derived from the 11 regions. The results supported HPLC data showing that all J. rigida samples exhibit considerable variations in phenolic profiles, and the samples were further clustered into three major groups coincident with their geographical regions of origin. In addition, two discriminant functions with a 100% discrimination ratio were constructed to further distinguish and classify samples with unknown membership on the basis of eigenvalues to allow optimal discrimination among the groups. Our comprehensive findings on matching phenolic profiles and bioactivities along with data from fingerprint chromatograms with chemometrics provide an effective tool for screening and quality evaluation of J. rigida and related medicinal preparations. PMID:28469573

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Neither insects nor wind: ambophily in dioecious Chamaedorea palms (Arecaceae).

PubMed

Rios, L D; Fuchs, E J; Hodel, D R; Cascante-Marín, A

2014-07-01

Pollination of Neotropical dioecious trees is commonly related to generalist insects. Similar data for non-tree species with separated genders are inconclusive. Recent studies on pollination of dioecious Chamaedorea palms (Arecaceae) suggest that species are either insect- or wind-pollinated. However, the wide variety of inflorescence and floral attributes within the genus suggests mixed pollination mode involving entomophily and anemophily. To evaluate this hypothesis, we studied the pollination of Chamaedorea costaricana, C. macrospadix, C. pinnatifrons and C. tepejilote in two montane forests in Costa Rica. A complementary morphological analysis of floral traits was carried out to distinguish species groups within the genus according to their most probable pollination mechanism. We conducted pollinator exclusion experiments, field observations on visitors to pistillate and staminate inflorescences, and trapped airborne pollen. A cluster analysis using 18 floral traits selected for their association with wind and insect pollination syndromes was carried out using 52 Chamaedorea species. Exclusion experiments showed that both wind and insects, mostly thrips (Thysanoptera), pollinated the studied species. Thrips used staminate inflorescences as brood sites and pollinated pistillate flowers by deception. Insects caught on pistillate inflorescences transported pollen, while traps proved that pollen is wind-borne. Our empirical findings clearly suggest that pollination of dioecious Chamaedorea palms is likely to involve both insects and wind. A cluster analysis showed that the majority of studied species have a combination of floral traits that allow for both pollination modes. Our pollination experiments and morphological analysis both suggest that while some species may be completely entomophilous or anemophilous, ambophily might be a common condition within Chamaedorea. Our results propose a higher diversity of pollination mechanisms of Neotropical dioecious species than previously suggested. © 2013 German Botanical Society and The Royal Botanical Society of the Netherlands.

Natural or Induced: Identifying Natural and Induced Swarms from Pre-production and Co-production Microseismic Catalogs at the Coso Geothermal Field

USGS Publications Warehouse

Schoenball, Martin; Kaven, Joern; Glen, Jonathan M. G.; Davatzes, Nicholas C.

2015-01-01

Increased levels of seismicity coinciding with injection of reservoir fluids have prompted interest in methods to distinguish induced from natural seismicity. Discrimination between induced and natural seismicity is especially difficult in areas that have high levels of natural seismicity, such as the geothermal fields at the Salton Sea and Coso, both in California. Both areas show swarm-like sequences that could be related to natural, deep fluid migration as part of the natural hydrothermal system. Therefore, swarms often have spatio-temporal patterns that resemble fluid-induced seismicity, and might possibly share other characteristics. The Coso Geothermal Field and its surroundings is one of the most seismically active areas in California with a large proportion of its activity occurring as seismic swarms. Here we analyze clustered seismicity in and surrounding the currently produced reservoir comparatively for pre-production and co-production periods. We perform a cluster analysis, based on the inter-event distance in a space-time-energy domain to identify notable earthquake sequences. For each event j, the closest previous event i is identified and their relationship categorized. If this nearest neighbor’s distance is below a threshold based on the local minimum of the bimodal distribution of nearest neighbor distances, then the event j is included in the cluster as a child to this parent event i. If it is above the threshold, event j begins a new cluster. This process identifies subsets of events whose nearest neighbor distances and relative timing qualify as a cluster as well as a characterizing the parent-child relationships among events in the cluster. We apply this method to three different catalogs: (1) a two-year microseismic survey of the Coso geothermal area that was acquired before exploration drilling in the area began; (2) the HYS_catalog_2013 that contains 52,000 double-difference relocated events and covers the years 1981 to 2013; and (3) a catalog of 57,000 events with absolute locations from the local network recorded between 2002 and 2007. Using this method we identify 10 clusters of more than 20 events each in the pre-production survey and more than 200 distinct seismicity clusters that each contain at least 20 and up to more than 1000 earthquakes in the more extensive catalogs. The cluster identification method used yields a hierarchy of links between multiple generations of parent and offspring events. We analyze different topological parameters of this hierarchy to better characterize and thus differentiate natural swarms from induced clustered seismicity and also to identify aftershock sequences of notable mainshocks. We find that the branching characteristic given by the average number of child events per parent event is significantly different for clusters below than for clusters around the produced field.

Whole genome sequencing of Brucella melitensis isolated from 57 patients in Germany reveals high diversity in strains from Middle East

PubMed Central

Georgi, Enrico; Walter, Mathias C.; Pfalzgraf, Marie-Theres; Northoff, Bernd H.; Holdt, Lesca M.; Scholz, Holger C.; Zoeller, Lothar

2017-01-01

Brucellosis, a worldwide common bacterial zoonotic disease, has become quite rare in Northern and Western Europe. However, since 2014 a significant increase of imported infections caused by Brucella (B.) melitensis has been noticed in Germany. Patients predominantly originated from Middle East including Turkey and Syria. These circumstances afforded an opportunity to gain insights into the population structure of Brucella strains. Brucella-isolates from 57 patients were recovered between January 2014 and June 2016 with culture confirmed brucellosis by the National Consultant Laboratory for Brucella. Their whole genome sequences were generated using the Illumina MiSeq platform. A whole genome-based SNP typing assay was developed in order to resolve geographically attributed genetic clusters. Results were compared to MLVA typing results, the current gold-standard of Brucella typing. In addition, sequences were examined for possible genetic variation within target regions of molecular diagnostic assays. Phylogenetic analyses revealed spatial clustering and distinguished strains from different patients in either case, whereas multiple isolates from a single patient or technical replicates showed identical SNP and MLVA profiles. By including WGS data from the NCBI database, five major genotypes were identified. Notably, strains originating from Turkey showed a high diversity and grouped into seven subclusters of genotype II. MLVA analysis congruently clustered all isolates and predominantly matched the East Mediterranean genetic clade. This study confirms whole-genome based SNP-analysis as a powerful tool for accurate typing of B. melitensis. Furthermore it allows special allocation and therefore provides useful information on the geographic origin for trace-back analysis. However, the lack of reliable metadata in public databases often prevents a resolution below geographic regions or country levels and corresponding precise trace-back analysis. Once this obstacle is resolved, WGS-derived bacterial typing adds an important method to complement epidemiological surveys during outbreak investigations. This is the first report of a detailed genetic investigation of an extensive collection of B. melitensis strains isolated from human cases in Germany. PMID:28388689

Analysis of X-ray structures of matrix metalloproteinases via chaotic map clustering.

PubMed

Giangreco, Ilenia; Nicolotti, Orazio; Carotti, Angelo; De Carlo, Francesco; Gargano, Gianfranco; Bellotti, Roberto

2010-10-08

Matrix metalloproteinases (MMPs) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. With this in mind, the perception of the intimate relationships among diverse MMPs could be a solid basis for accelerated learning in designing new selective MMP inhibitors. In this regard, decrypting the latent molecular reasons in order to elucidate similarity among MMPs is a key challenge. We describe a pairwise variant of the non-parametric chaotic map clustering (CMC) algorithm and its application to 104 X-ray MMP structures. In this analysis electrostatic potentials are computed and used as input for the CMC algorithm. It was shown that differences between proteins reflect genuine variation of their electrostatic potentials. In addition, the analysis has been also extended to analyze the protein primary structures and the molecular shapes of the MMP co-crystallised ligands. The CMC algorithm was shown to be a valuable tool in knowledge acquisition and transfer from MMP structures. Based on the variation of electrostatic potentials, CMC was successful in analysing the MMP target family landscape and different subsites. The first investigation resulted in rational figure interpretation of both domain organization as well as of substrate specificity classifications. The second made it possible to distinguish the MMP classes, demonstrating the high specificity of the S1' pocket, to detect both the occurrence of punctual mutations of ionisable residues and different side-chain conformations that likely account for induced-fit phenomena. In addition, CMC demonstrated a potential comparable to the most popular UPGMA (Unweighted Pair Group Method with Arithmetic mean) method that, at present, represents a standard clustering bioinformatics approach. Interestingly, CMC and UPGMA resulted in closely comparable outcomes, but often CMC produced more informative and more easy interpretable dendrograms. Finally, CMC was successful for standard pairwise analysis (i.e., Smith-Waterman algorithm) of protein sequences and was used to convincingly explain the complementarity existing between the molecular shapes of the co-crystallised ligand molecules and the accessible MMP void volumes.

Evidence for an Accretion Origin for the Outer Halo Globular Cluster System of M31

NASA Astrophysics Data System (ADS)

Mackey, A. D.; Huxor, A. P.; Ferguson, A. M. N.; Irwin, M. J.; Tanvir, N. R.; McConnachie, A. W.; Ibata, R. A.; Chapman, S. C.; Lewis, G. F.

2010-07-01

We use a sample of newly discovered globular clusters from the Pan-Andromeda Archaeological Survey (PAndAS) in combination with previously cataloged objects to map the spatial distribution of globular clusters in the M31 halo. At projected radii beyond ≈30 kpc, where large coherent stellar streams are readily distinguished in the field, there is a striking correlation between these features and the positions of the globular clusters. Adopting a simple Monte Carlo approach, we test the significance of this association by computing the probability that it could be due to the chance alignment of globular clusters smoothly distributed in the M31 halo. We find that the likelihood of this possibility is low, below 1%, and conclude that the observed spatial coincidence between globular clusters and multiple tidal debris streams in the outer halo of M31 reflects a genuine physical association. Our results imply that the majority of the remote globular cluster system of M31 has been assembled as a consequence of the accretion of cluster-bearing satellite galaxies. This constitutes the most direct evidence to date that the outer halo globular cluster populations in some galaxies are largely accreted. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii.

Climbing the Ladder of Star Formation Feedback

NASA Astrophysics Data System (ADS)

Frank, Adam

2012-10-01

While much is understood about isolated star formation, the opposite is true for star formation in clusters of both low and high mass. In particular the mechanisms by which many coevally formed stars affect their parent cloud environment remains poorly characterized. Fundamental questions such as interplay between multiple outflows, ionization fronts and turbulence are just beginning to be fully articulated. Distinguishing between the nature of feedback in clusters of different mass is also critical. In high mass clusters O stars are expected to dominate energetics while in low mass clusters multiple collimated outflows may represent the dominant feedback mechanism. Thus the issue of feedback modalities in clusters of different masses represents one of the major challenges to the next generation of star formation studies. In this proposal we seek to carry forward a focused theoretical study of feedback in both low and high-mass cluster environments with direct connections to observations. Using a state-of-the-art Adaptive Mesh Refinement MHD multi-physics code {developed by our group} we propose two computational studies: {1} multiple, interacting outflows and their role in altering the properties of a parent low mass cluster {2} Poorly collimated outburst/outflows from massive star{s} and their effect on high mass cluster star forming environments. In both cases we will use initial conditions derived from high-resolution AMR MHD simulations of cloud/cluster formation. Synthetic observations derived from the simulations {in a variety of emission lines from ions to atoms to molecules} will allow for direct contact with HST and other star formation databases.

Accessing the Vibrational Signatures of Amino Acid Ions Embedded in Water Clusters

DOE PAGES

Voss, Jonathan M.; Fischer, Kaitlyn C.; Garand, Etienne

2018-04-16

We present an infrared predissociation (IRPD) study of microsolvated GlyH +(H 2O) n and GlyH +(D 2O) n clusters, formed inside of a cryogenic ion trap via condensation of H 2O or D 2O onto the protonated glycine ions. The resulting IRPD spectra, showing characteristic O–H and O–D stretches, indicate that H/D exchange reactions are quenched when the ion trap is held at 80 K, minimizing the presence of isotopomers. Comparisons of GlyH +(H 2O) n and GlyH +(D 2O) n spectra clearly highlight and distinguish the vibrational signatures of the water solvent molecules from those of the core GlyHmore » + ion, allowing for quick assessment of solvation structures. Without the aid of calculations, we can already infer solvation motifs and the presence of multiple conformations. Furthermore, the use of a cryogenic ion trap to cluster solvent molecules around ions of interest and control H/D exchange reactions is broadly applicable and should be extendable to studies of more complex peptidic ions in large solvated clusters.« less

Accessing the Vibrational Signatures of Amino Acid Ions Embedded in Water Clusters

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

Voss, Jonathan M.; Fischer, Kaitlyn C.; Garand, Etienne

We present an infrared predissociation (IRPD) study of microsolvated GlyH +(H 2O) n and GlyH +(D 2O) n clusters, formed inside of a cryogenic ion trap via condensation of H 2O or D 2O onto the protonated glycine ions. The resulting IRPD spectra, showing characteristic O–H and O–D stretches, indicate that H/D exchange reactions are quenched when the ion trap is held at 80 K, minimizing the presence of isotopomers. Comparisons of GlyH +(H 2O) n and GlyH +(D 2O) n spectra clearly highlight and distinguish the vibrational signatures of the water solvent molecules from those of the core GlyHmore » + ion, allowing for quick assessment of solvation structures. Without the aid of calculations, we can already infer solvation motifs and the presence of multiple conformations. Furthermore, the use of a cryogenic ion trap to cluster solvent molecules around ions of interest and control H/D exchange reactions is broadly applicable and should be extendable to studies of more complex peptidic ions in large solvated clusters.« less

Non-Gaussian shape discrimination with spectroscopic galaxy surveys

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

Byun, Joyce; Bean, Rachel, E-mail: byun@astro.cornell.edu, E-mail: rbean@astro.cornell.edu

2015-03-01

We consider how galaxy clustering data, from Mpc to Gpc scales, from upcoming large scale structure surveys, such as Euclid and DESI, can provide discriminating information about the bispectrum shape arising from a variety of inflationary scenarios. Through exploring in detail the weighting of shape properties in the calculation of the halo bias and halo mass function we show how they probe a broad range of configurations, beyond those in the squeezed limit, that can help distinguish between shapes with similar large scale bias behaviors. We assess the impact, on constraints for a diverse set of non-Gaussian shapes, of galaxymore » clustering information in the mildly non-linear regime, and surveys that span multiple redshifts and employ different galactic tracers of the dark matter distribution. Fisher forecasts are presented for a Euclid-like spectroscopic survey of Hα-selected emission line galaxies (ELGs), and a DESI-like survey, of luminous red galaxies (LRGs) and [O-II] doublet-selected ELGs, in combination with Planck-like CMB temperature and polarization data.While ELG samples provide better probes of shapes that are divergent in the squeezed limit, LRG constraints, centered below z<1, yield stronger constraints on shapes with scale-independent large-scale halo biases, such as the equilateral template. The ELG and LRG samples provide complementary degeneracy directions for distinguishing between different shapes. For Hα-selected galaxies, we note that recent revisions of the expected Hα luminosity function reduce the halo bias constraints on the local shape, relative to the CMB. For galaxy clustering constraints to be comparable to those from the CMB, additional information about the Gaussian galaxy bias is needed, such as can be determined from the galaxy clustering bispectrum or probing the halo power spectrum directly through weak lensing. If the Gaussian galaxy bias is constrained to better than a percent level then the LSS and CMB data could provide complementary constraints that will enable differentiation of bispectrum with distinct theoretical origins but with similar large scale, squeezed-limit properties.« less

Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study

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

Pang, Yuanjie, E-mail: yuanjie.p@gmail.com

Background: Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). Methods: We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000–2002) and 277 American Indian participants in SHS (1998–2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysismore » (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). Results: All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. Conclusions: In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US. - Highlights: • We identified and compared environmental sources of urinary metals in MESA and SHS. • ∑As-U-W in SHS may reflect groundwater contamination in rural areas. • Cd-Zn in SHS may reflect common sources from meat products or metabolic interaction. • ∑As, U, W, and Zn differed the most between MESA and SHS participants.« less

Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation

PubMed Central

Badal, Brateil; Solovyov, Alexander; Di Cecilia, Serena; Chan, Joseph Minhow; Chang, Li-Wei; Iqbal, Ramiz; Aydin, Iraz T.; Rajan, Geena S.; Chen, Chen; Abbate, Franco; Arora, Kshitij S.; Tanne, Antoine; Gruber, Stephen B.; Johnson, Timothy M.; Fullen, Douglas R.; Phelps, Robert; Bhardwaj, Nina; Bernstein, Emily; Ting, David T.; Brunner, Georg; Schadt, Eric E.; Greenbaum, Benjamin D.; Celebi, Julide Tok

2017-01-01

BACKGROUND. Melanoma is a heterogeneous malignancy. We set out to identify the molecular underpinnings of high-risk melanomas, those that are likely to progress rapidly, metastasize, and result in poor outcomes. METHODS. We examined transcriptome changes from benign states to early-, intermediate-, and late-stage tumors using a set of 78 treatment-naive melanocytic tumors consisting of primary melanomas of the skin and benign melanocytic lesions. We utilized a next-generation sequencing platform that enabled a comprehensive analysis of protein-coding and -noncoding RNA transcripts. RESULTS. Gene expression changes unequivocally discriminated between benign and malignant states, and a dual epigenetic and immune signature emerged defining this transition. To our knowledge, we discovered previously unrecognized melanoma subtypes. A high-risk primary melanoma subset was distinguished by a 122-epigenetic gene signature (“epigenetic” cluster) and TP53 family gene deregulation (TP53, TP63, and TP73). This subtype associated with poor overall survival and showed enrichment of cell cycle genes. Noncoding repetitive element transcripts (LINEs, SINEs, and ERVs) that can result in immunostimulatory signals recapitulating a state of “viral mimicry” were significantly repressed. The high-risk subtype and its poor predictive characteristics were validated in several independent cohorts. Additionally, primary melanomas distinguished by specific immune signatures (“immune” clusters) were identified. CONCLUSION. The TP53 family of genes and genes regulating the epigenetic machinery demonstrate strong prognostic and biological relevance during progression of early disease. Gene expression profiling of protein-coding and -noncoding RNA transcripts may be a better predictor for disease course in melanoma. This study outlines the transcriptional interplay of the cancer cell’s epigenome with the immune milieu with potential for future therapeutic targeting. FUNDING. National Institutes of Health (CA154683, CA158557, CA177940, CA087497-13), Tisch Cancer Institute, Melanoma Research Foundation, the Dow Family Charitable Foundation, and the Icahn School of Medicine at Mount Sinai. PMID:28469092

Classification of immature mosquito species according to characteristics of the larval habitat in the subtropical province of Chaco, Argentina.

PubMed

Stein, Marina; Ludueña-Almeida, Francisco; Willener, Juana Alicia; Almirón, Walter Ricardo

2011-06-01

To classify mosquito species based on common features of their habitats, samples were obtained fortnightly between June 2001-October 2003 in the subtropical province of Chaco, Argentina. Data on the type of larval habitat, nature of the habitat (artificial or natural), size, depth, location related to sunlight, distance to the neighbouring houses, type of substrate, organic material, vegetation and algae type and their presence were collected. Data on the permanence, temperature, pH, turbidity, colour, odour and movement of the larval habitat's water were also collected. From the cluster analysis, three groups of species associated by their degree of habitat similarity were obtained and are listed below. Group 1 consisted of Aedes aegypti. Group 2 consisted of Culex imitator, Culex davisi, Wyeomyia muehlensi and Toxorhynchites haemorrhoidalis separatus. Within group 3, two subgroups are distinguished: A (Psorophora ferox, Psorophora cyanescens, Psorophora varinervis, Psorophora confinnis, Psorophora cingulata, Ochlerotatus hastatus-oligopistus, Ochlerotatus serratus, Ochlerotatus scapularis, Culex intrincatus, Culex quinquefasciatus, Culex pilosus, Ochlerotatus albifasciatus, Culex bidens) and B (Culex maxi, Culex eduardoi, Culex chidesteri, Uranotaenia lowii, Uranotaenia pulcherrima, Anopheles neomaculipalpus, Anopheles triannulatus, Anopheles albitarsis, Uranotaenia apicalis, Mansonia humeralis and Aedeomyia squamipennis). Principal component analysis indicates that the size of the larval habitats and the presence of aquatic vegetation are the main characteristics that explain the variation among different species. In contrast, water permanence is second in importance. Water temperature, pH and the type of larval habitat are less important in explaining the clustering of species.

Genetic diversity and relationships among different tomato varieties revealed by EST-SSR markers.

PubMed

Korir, N K; Diao, W; Tao, R; Li, X; Kayesh, E; Li, A; Zhen, W; Wang, S

2014-01-08

The genetic diversity and relationship of 42 tomato varieties sourced from different geographic regions was examined with EST-SSR markers. The genetic diversity was between 0.18 and 0.77, with a mean of 0.49; the polymorphic information content ranged from 0.17 to 0.74, with a mean of 0.45. This indicates a fairly high degree of diversity among these tomato varieties. Based on the cluster analysis using unweighted pair-group method with arithmetic average (UPGMA), all the tomato varieties fell into 5 groups, with no obvious geographical distribution characteristics despite their diverse sources. The principal component analysis (PCA) supported the clustering result; however, relationships among varieties were more complex in the PCA scatterplot than in the UPGMA dendrogram. This information about the genetic relationships between these tomato lines helps distinguish these 42 varieties and will be useful for tomato variety breeding and selection. We confirm that the EST-SSR marker system is useful for studying genetic diversity among tomato varieties. The high degree of polymorphism and the large number of bands obtained per assay shows that SSR is the most informative marker system for tomato genotyping for purposes of rights/protection and for the tomato industry in general. It is recommended that these varieties be subjected to identification using an SSR-based manual cultivar identification diagram strategy or other easy-to-use and referable methods so as to provide a complete set of information concerning genetic relationships and a readily usable means of identifying these varieties.

A Wide Variety of Clostridium perfringens Type A Food-Borne Isolates That Carry a Chromosomal cpe Gene Belong to One Multilocus Sequence Typing Cluster

PubMed Central

Xiao, Yinghua; Wagendorp, Arjen; Moezelaar, Roy; Abee, Tjakko

2012-01-01

Of 98 suspected food-borne Clostridium perfringens isolates obtained from a nationwide survey by the Food and Consumer Product Safety Authority in The Netherlands, 59 strains were identified as C. perfringens type A. Using PCR-based techniques, the cpe gene encoding enterotoxin was detected in eight isolates, showing a chromosomal location for seven isolates and a plasmid location for one isolate. Further characterization of these strains by using (GTG)5 fingerprint repetitive sequence-based PCR analysis distinguished C. perfringens from other sulfite-reducing clostridia but did not allow for differentiation between various types of C. perfringens strains. To characterize the C. perfringens strains further, multilocus sequence typing (MLST) analysis was performed on eight housekeeping genes of both enterotoxic and non-cpe isolates, and the data were combined with a previous global survey covering strains associated with food poisoning, gas gangrene, and isolates from food or healthy individuals. This revealed that the chromosomal cpe strains (food strains and isolates from food poisoning cases) belong to a distinct cluster that is significantly distant from all the other cpe plasmid-carrying and cpe-negative strains. These results suggest that different groups of C. perfringens have undergone niche specialization and that a distinct group of food isolates has specific core genome sequences. Such findings have epidemiological and evolutionary significance. Better understanding of the origin and reservoir of enterotoxic C. perfringens may allow for improved control of this organism in foods. PMID:22865060

Multilocus variable-number tandem repeat analysis distinguishes outbreak and sporadic Escherichia coli O157:H7 isolates.

PubMed

Noller, Anna C; McEllistrem, M Catherine; Pacheco, Antonio G F; Boxrud, David J; Harrison, Lee H

2003-12-01

Escherichia coli O157:H7 is a major cause of food-borne illness in the United States. Outbreak detection involves traditional epidemiological methods and routine molecular subtyping by pulsed-field gel electrophoresis (PFGE). PFGE is labor-intensive, and the results are difficult to analyze and not easily transferable between laboratories. Multilocus variable-number tandem repeat (VNTR) analysis (MLVA) is a fast, portable method that analyzes multiple VNTR loci, which are areas of the bacterial genome that evolve quickly. Eighty isolates, including 21 isolates from five epidemiologically well-characterized outbreaks from Pennsylvania and Minnesota, were analyzed by PFGE and MLVA. Strains in PFGE clusters were defined as strains that differed by less than or equal to one band by using XbaI and the confirmatory enzyme SpeI. MLVA was performed by comparing the number of tandem repeats at seven loci. From 6 to 30 alleles were found at the seven loci, resulting in 64 MLVA types among the 80 isolates. MLVA correctly identified the isolates from all five outbreaks if only a single-locus variant was allowed. MLVA differentiated strains with unique PFGE types. Additionally, MLVA discriminated strains within PFGE-defined clusters that were not known to be part of an outbreak. In addition to being a simple and validated method for E. coli O157:H7 outbreak detection, MLVA appears to have a sensitivity equal to that of PFGE and a specificity superior to that of PFGE.

Root-derived auxin contributes to the phosphorus-deficiency-induced cluster-root formation in white lupin (Lupinus albus).

PubMed

Meng, Zhi Bin; You, Xue Di; Suo, Dong; Chen, Yun Long; Tang, Caixian; Yang, Jian Li; Zheng, Shao Jian

2013-08-01

Formation of cluster roots is a typical morphological response to phosphorus (P) deficiency in white lupin (Lupinus albus), but its physiological and molecular mechanisms are still unclear. We investigated the role of auxin in the initiation of cluster roots by distinguishing the sources of auxin, measuring the longitudinal distribution patterns of free indole-3-acetic acid (IAA) along the root and the related gene expressions responsible for polar auxin transport (PAT) in different developmental stages of cluster roots. We found that removal of shoot apex or primary root apex and application of auxin-influx or -efflux transport inhibitors, 3-chloro-4-hydroxyphenylacetic acid, N-1-naphthylphthalamic acid and 2,3,5-triiodobenzoic acid, to the stem did not affect the number of cluster roots and the free-IAA concentration in the roots of P-deficient plants, but when these inhibitors were applied directly to the growth media, the cluster-root formation was greatly suppressed, suggesting the fundamental role of root-derived IAA in cluster-root formation. The concentration of free IAA in the roots was higher in P-deficient plants than in P-adequate ones, and the highest in the lateral-root apex and the lowest in the mature cluster roots. Meanwhile the expression patterns of LaAUX1, LaPIN1 and LaPIN3 transcripts related to PAT was consistent with concentrations of free IAA along the lateral root, indicating the contribution of IAA redistribution in the cluster-root development. We proposed that root-derived IAA plays a direct and important role in the P-deficiency-induced formation of cluster roots. Copyright © Physiologia Plantarum 2012.

Characteristics and Classification of Least Altered Streamflows in Massachusetts

USGS Publications Warehouse

Armstrong, David S.; Parker, Gene W.; Richards, Todd A.

2008-01-01

Streamflow records from 85 streamflow-gaging stations at which streamflows were considered to be least altered were used to characterize natural streamflows within southern New England. Period-of-record streamflow data were used to determine annual hydrographs of median monthly flows. The shapes and magnitudes of annual hydrographs of median monthly flows, normalized by drainage area, differed among stations in different geographic areas of southern New England. These differences were gradational across southern New England and were attributed to differences in basin and climate characteristics. Period-of-record streamflow data were also used to analyze the statistical properties of daily streamflows at 61 stations across southern New England by using L-moment ratios. An L-moment ratio diagram of L-skewness and L-kurtosis showed a continuous gradation in these properties between stations and indicated differences between base-flow dominated and runoff-dominated rivers. Streamflow records from a concurrent period (1960-2004) for 61 stations were used in a multivariate statistical analysis to develop a hydrologic classification of rivers in southern New England. Missing records from 46 of these stations were extended by using a Maintenance of Variation Extension technique. The concurrent-period streamflows were used in the Indicators of Hydrologic Alteration and Hydrologic Index Tool programs to determine 224 hydrologic indices for the 61 stations. Principal-components analysis (PCA) was used to reduce the number of hydrologic indices to 20 that provided nonredundant information. The PCA also indicated that the major patterns of variability in the dataset are related to differences in flow variability and low-flow magnitude among the stations. Hierarchical cluster analysis was used to classify stations into groups with similar hydrologic properties. The cluster analysis classified rivers in southern New England into two broad groups: (1) base-flow dominated rivers, whose statistical properties indicated less flow variability and high magnitudes of low flow, and (2) runoff-dominated rivers, whose statistical properties indicated greater flow variability and lower magnitudes of low flow. A four-cluster classification further classified the runoff-dominated streams into three groups that varied in gradient, elevation, and differences in winter streamflow conditions: high-gradient runoff-dominated rivers, northern runoff-dominated rivers, and southern runoff-dominated rivers. A nine-cluster division indicated that basin size also becomes a distinguishing factor among basins at finer levels of classification. Smaller basins (less than 10 square miles) were classified into different groups than larger basins. A comparison of station classifications indicated that a classification based on multiple hydrologic indices that represent different aspects of the flow regime did not result in the same classification of stations as a classification based on a single type of statistic such as a monthly median. River basins identified by the cluster analysis as having similar hydrologic properties tended to have similar basin and climate characteristics and to be in close proximity to one another. Stations were not classified in the same cluster on the basis of geographic location alone; as a result, boundaries cannot be drawn between geographic regions with similar streamflow characteristics. Rivers with different basin and climate characteristics were classified in different clusters, even if they were in adjacent basins or upstream and downstream within the same basin.

A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle

PubMed Central

Bolormaa, Sunduimijid; Pryce, Jennie E.; Reverter, Antonio; Zhang, Yuandan; Barendse, William; Kemper, Kathryn; Tier, Bruce; Savin, Keith; Hayes, Ben J.; Goddard, Michael E.

2014-01-01

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V−1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups. PMID:24675618

Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.

PubMed

Suslick, Benjamin A; Feng, Liang; Suslick, Kenneth S

2010-03-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 degrees C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.

Comparative genetic analysis of trichome-less and normal pod genotypes of Mucuna pruriens (Fabaceae).

PubMed

Dhawan, S S; Rai, G K; Darokar, M P; Lal, R K; Misra, H O; Khanuja, S P S

2011-09-15

Velvet bean (Mucuna pruriens) seeds contain the catecholic amino acid L-DoPA (L-3,4-dihydroxyphenylalanine), which is a neurotransmitter precursor and used for the treatment of Parkinson's disease and mental disorders. The great demand for L-DoPA is largely met by the pharmaceutical industry through extraction of the compound from wild populations of this plant; commercial exploitation of this compound is hampered because of its limited availability. The trichomes present on the pods can cause severe itching, blisters and dermatitis, discouraging cultivation. We screened genetic stocks of velvet bean for the trichome-less trait, along with high seed yield and L-DoPA content. The highest yielding trichome-less elite strain was selected and indentified on the basis of a PCR-based DNA fingerprinting method (RAPD), using deca-nucleotide primers. A genetic similarity index matrix was obtained through multivariant analysis using Nei and Li's coefficient. The similarity coefficients were used to generate a tree for cluster analysis using the UPGMA method. Analysis of amplification spectra of 408 bands obtained with 56 primers allowed us to distinguish a trichome-less elite strain of M. pruriens.

Discrimination of Complex Mixtures by a Colorimetric Sensor Array: Coffee Aromas

PubMed Central

Suslick, Benjamin A.; Feng, Liang; Suslick, Kenneth S.

2010-01-01

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 °C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures. PMID:20143838

Capillary electrophoresis fingerprinting and spectrophotometric determination of antioxidant potential for classification of Mentha products.

PubMed

Roblová, Vendula; Bittová, Miroslava; Kubáň, Petr; Kubáň, Vlastimil

2016-07-01

In this work aqueous infusions from ten Mentha herbal samples (four different Mentha species and six hybrids of Mentha x piperita) and 20 different peppermint teas were screened by capillary electrophoresis with UV detection. The fingerprint separation was accomplished in a 25 mM borate background electrolyte with 10% methanol at pH 9.3. The total polyphenolic content in the extracts was determined spectrophotometrically at 765 nm by a Folin-Ciocalteu phenol assay. Total antioxidant activity was determined by scavenging of 2,2-diphenyl-1-picrylhydrazyl radical at 515 nm. The peak areas of 12 dominant peaks from CE analysis, present in all samples, and the value of total polyphenolic content and total antioxidant activity obtained by spectrophotometry was combined into a single data matrix and principal component analysis was applied. The obtained principal component analysis model resulted in distinct clusters of Mentha and peppermint tea samples distinguishing the samples according to their potential protective antioxidant effect. Principal component analysis, using a non-targeted approach with no need for compound identification, was found as a new promising tool for the screening of herbal tea products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative allochem compositional analysis of Lochkovian-Pragian boundary sections in the Prague Basin (Czech Republic)

NASA Astrophysics Data System (ADS)

Weinerová, Hedvika; Hron, Karel; Bábek, Ondřej; Šimíček, Daniel; Hladil, Jindřich

2017-06-01

Quantitative allochem compositional trends across the Lochkovian-Pragian boundary Event were examined at three sections recording the proximal to more distal carbonate ramp environment of the Prague Basin. Multivariate statistical methods (principal component analysis, correspondence analysis, cluster analysis) of point-counted thin section data were used to reconstruct facies stacking patterns and sea-level history. Both the closed-nature allochem percentages and their centred log-ratio (clr) coordinates were used. Both these approaches allow for distinguishing of lowstand, transgressive and highstand system tracts within the Praha Formation, which show gradual transition from crinoid-dominated facies deposited above the storm wave base to dacryoconarid-dominated facies of deep-water environment below the storm wave base. Quantitative compositional data also indicate progradative-retrogradative trends in the macrolithologically monotonous shallow-water succession and enable its stratigraphic correlation with successions from deeper-water environments. Generally, the stratigraphic trends of the clr data are more sensitive to subtle changes in allochem composition in comparison to the results based on raw data. A heterozoan-dominated allochem association in shallow-water environments of the Praha Formation supports the carbonate ramp environment assumed by previous authors.

The Origin of Dwarf Early-Type Galaxies

NASA Astrophysics Data System (ADS)

Toloba, Elisa; Boselli, A.; Gorgas, J.

2013-01-01

The physical mechanisms involved in the formation and evolution of dwarf early-type galaxies (dEs) are not well understood yet. Whether these objects, that outnumber any other class of object in clusters, are the low luminosity extension of massive early-type galaxies, i.e. formed through similar processes, or are a different group of objects possibly formed through the transformation of low luminosity spiral galaxies, is still an open debate. Studying the kinematic properties of dEs is a powerful way to distinguish between these two scenarios. In my PhD, awarded with a Fulbright postdoctoral Fellowship and with the 2011 prize to the best Spanish PhD dissertation in Astronomy, we used this technique to make a spectrophotometric analysis of 18 dEs in the Virgo cluster. I found some differences for these dEs within the cluster. The dEs in the outer parts of Virgo have rotation curves with shapes and amplitudes similar to late-type galaxies of the same luminosity. They are rotationally supported, have disky isophotes, and younger ages than those dEs in the center of Virgo, which are pressure supported, often have boxy isophotes and are older. Ram pressure stripping, which removes the gas of galaxies leaving the stars untouched, explains the properties of the dEs located in the outskirts of Virgo. However, the dEs in the central cluster regions, which have lost their angular momentum, must have suffered a more violent transformation. A combination of ram pressure stripping and harassment is not enough to remove the rotation and the disky structures of these galaxies. I am conducting new analysis with 20 new dEs to throw some light in this direction. I also analysed the Faber-Jackson and the Fundamental Plane relations, and I found that dEs deviate from the trends of massive elliptical galaxies towards the position of dark matter dominated systems such as the dwarf spheroidal satellites of the Milky Way and M31. This indicates that dEs have a non-negligible dark matter fraction within their half light radius, we used these diagrams to quantify this dark matter content, which is ~40%, significantly larger than previously thought for these kind of objects.

Genetic structure in four West African population groups

PubMed Central

Adeyemo, Adebowale A; Chen, Guanjie; Chen, Yuanxiu; Rotimi, Charles

2005-01-01

Background Africa contains the most genetically divergent group of continental populations and several studies have reported that African populations show a high degree of population stratification. In this regard, it is important to investigate the potential for population genetic structure or stratification in genetic epidemiology studies involving multiple African populations. The presences of genetic sub-structure, if not properly accounted for, have been reported to lead to spurious association between a putative risk allele and a disease. Within the context of the Africa America Diabetes Mellitus (AADM) Study (a genetic epidemiologic study of type 2 diabetes mellitus in West Africa), we have investigated population structure or stratification in four ethnic groups in two countries (Akan and Gaa-Adangbe from Ghana, Yoruba and Igbo from Nigeria) using data from 372 autosomal microsatellite loci typed in 493 unrelated persons (986 chromosomes). Results There was no significant population genetic structure in the overall sample. The smallest probability is associated with an inferred cluster of 1 and little of the posterior probability is associated with a higher number of inferred clusters. The distribution of members of the sample to inferred clusters is consistent with this finding; roughly the same proportion of individuals from each group is assigned to each cluster with little variation between the ethnic groups. Analysis of molecular variance (AMOVA) showed that the between-population component of genetic variance is less than 0.1% in contrast to 99.91% for the within population component. Pair-wise genetic distances between the four ethnic groups were also very similar. Nonetheless, the small between-population genetic variance was sufficient to distinguish the two Ghanaian groups from the two Nigerian groups. Conclusion There was little evidence for significant population substructure in the four major West African ethnic groups represented in the AADM study sample. Ethnicity apparently did not introduce differential allele frequencies that may affect analysis and interpretation of linkage and association studies. These findings, although not entirely surprising given the geographical proximity of these groups, provide important insights into the genetic relationships between the ethnic groups studied and confirm previous results that showed close genetic relationship between most studied West African groups. PMID:15978124

Multilevel Classification of PISA 2015 Research Participant Countries' Literacy and These Classes' Relationship with Information and Communication Technologies

ERIC Educational Resources Information Center

Yalcin, Seher

2018-01-01

In this study, it is aimed to distinguish the reading skills of students participating in PISA 2015 application into multi-level latent classes at the student and country level. Furthermore, it is aimed to examine how the clusters emerged at country-level is predicted by variables as students have the information and communication technology (ICT)…

Observations of movement dynamics of flying insects using high resolution lidar.

PubMed

Kirkeby, Carsten; Wellenreuther, Maren; Brydegaard, Mikkel

2016-07-04

Insects are fundamental to ecosystem functioning and biodiversity, yet the study of insect movement, dispersal and activity patterns remains a challenge. Here we present results from a novel high resolution laser-radar (lidar) system for quantifying flying insect abundance recorded during one summer night in Sweden. We compare lidar recordings with data from a light trap deployed alongside the lidar. A total of 22808 insect were recorded, and the relative temporal quantities measured matched the quantities recorded with the light trap within a radius of 5 m. Lidar records showed that small insects (wing size <2.5 mm(2) in cross-section) moved across the field and clustered near the light trap around 22:00 local time, while larger insects (wing size >2.5 mm(2) in cross-section) were most abundant near the lidar beam before 22:00 and then moved towards the light trap between 22:00 and 23:30. We could distinguish three insect clusters based on morphology and found that two contained insects predominantly recorded above the field in the evening, whereas the third was formed by insects near the forest at around 21:30. Together our results demonstrate the capability of lidar for distinguishing different types of insect during flight and quantifying their movements.

Observations of movement dynamics of flying insects using high resolution lidar

PubMed Central

Kirkeby, Carsten; Wellenreuther, Maren; Brydegaard, Mikkel

2016-01-01

Insects are fundamental to ecosystem functioning and biodiversity, yet the study of insect movement, dispersal and activity patterns remains a challenge. Here we present results from a novel high resolution laser-radar (lidar) system for quantifying flying insect abundance recorded during one summer night in Sweden. We compare lidar recordings with data from a light trap deployed alongside the lidar. A total of 22808 insect were recorded, and the relative temporal quantities measured matched the quantities recorded with the light trap within a radius of 5 m. Lidar records showed that small insects (wing size <2.5 mm2 in cross-section) moved across the field and clustered near the light trap around 22:00 local time, while larger insects (wing size >2.5 mm2 in cross-section) were most abundant near the lidar beam before 22:00 and then moved towards the light trap between 22:00 and 23:30. We could distinguish three insect clusters based on morphology and found that two contained insects predominantly recorded above the field in the evening, whereas the third was formed by insects near the forest at around 21:30. Together our results demonstrate the capability of lidar for distinguishing different types of insect during flight and quantifying their movements. PMID:27375089

Using Cluster Analysis to Examine Husband-Wife Decision Making

ERIC Educational Resources Information Center

Bonds-Raacke, Jennifer M.

2006-01-01

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

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

Context. Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims: We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods: We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional probability density function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results: A clustering analysis based only on the J = 1-0 lines of three isotopologues of CO proves sufficient to reveal distinct density/column density regimes (nH 100 cm-3, 500 cm-3, and >1000 cm-3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1-0 line of HCO+ and the N = 1-0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH 7 × 103 cm-3, 4 × 104 cm-3) for the 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. 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

Phylogenomic Analysis of Lactobacillus curvatus Reveals Two Lineages Distinguished by Genes for Fermenting Plant-Derived Carbohydrates.

PubMed

Terán, Lucrecia C; Coeuret, Gwendoline; Raya, Raúl; Zagorec, Monique; Champomier-Vergès, Marie-Christine; Chaillou, Stéphane

2018-06-01

Lactobacillus curvatus is a lactic acid bacterium encountered in many different types of fermented food (meat, seafood, vegetables, and cereals). Although this species plays an important role in the preservation of these foods, few attempts have been made to assess its genomic diversity. This study uses comparative analyses of 13 published genomes (complete or draft) to better understand the evolutionary processes acting on the genome of this species. Phylogenomic analysis, based on a coalescent model of evolution, revealed that the 6,742 sites of single nucleotide polymorphism within the L. curvatus core genome delineate two major groups, with lineage 1 represented by the newly sequenced strain FLEC03, and lineage 2 represented by the type-strain DSM20019. The two lineages could also be distinguished by the content of their accessory genome, which sheds light on a long-term evolutionary process of lineage-dependent genetic acquisition and the possibility of population structure. Interestingly, one clade from lineage 2 shared more accessory genes with strains of lineage 1 than with other strains of lineage 2, indicating recent convergence in carbohydrate catabolism. Both lineages had a wide repertoire of accessory genes involved in the fermentation of plant-derived carbohydrates that are released from polymers of α/β-glucans, α/β-fructans, and N-acetylglucosan. Other gene clusters were distributed among strains according to the type of food from which the strains were isolated. These results give new insight into the ecological niches in which L. curvatus may naturally thrive (such as silage or compost heaps) in addition to fermented food.

GyrB sequence analysis and MALDI-TOF MS as identification tools for plant pathogenic Clavibacter.

PubMed

Zaluga, Joanna; Heylen, Kim; Van Hoorde, Koenraad; Hoste, Bart; Van Vaerenbergh, Johan; Maes, Martine; De Vos, Paul

2011-09-01

The bacterial genus Clavibacter has only one species, Clavibacter michiganensis, containing five subspecies. All five are plant pathogens, among which three are recognized as quarantine pests (mentioned on the EPPO A2 list). Prevention of their introduction and epidemic outbreaks requires a reliable and accurate identification. Currently, identification of these bacteria is time consuming and often problematic, mainly because of cross-reactions with other plant-associated bacteria in immunological tests and false-negative results in PCR detection methods. Furthermore, distinguishing closely related subspecies is not straightforward. This study aimed at evaluating the use of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and a fragment of the gyrB sequence for the reliable and fast identification of the Clavibacter subspecies. Amplification and sequencing of gyrB using a single primer set had sufficient resolution and specificity to identify each subspecies based on both sequence similarities in cluster analyses and specific signatures within the sequences. All five subspecies also generated distinct and reproducible MALDI-TOF MS profiles, with unique and specific ion peaks for each subspecies, which could be used as biomarkers for identification. Results from both methods were in agreement and were able to distinguish the five Clavibacter subspecies from each other and from representatives of closely related Rathayibacter, Leifsonia or Curtobacterium species. Our study suggests that proteomic analysis using MALDI-TOF MS and gyrB sequence are powerful diagnostic tools for the accurate identification of Clavibacter plant pathogens. Copyright © 2011 Elsevier GmbH. All rights reserved.

Density-based cluster algorithms for the identification of core sets

NASA Astrophysics Data System (ADS)

Lemke, Oliver; Keller, Bettina G.

2016-10-01

The core-set approach is a discretization method for Markov state models of complex molecular dynamics. Core sets are disjoint metastable regions in the conformational space, which need to be known prior to the construction of the core-set model. We propose to use density-based cluster algorithms to identify the cores. We compare three different density-based cluster algorithms: the CNN, the DBSCAN, and the Jarvis-Patrick algorithm. While the core-set models based on the CNN and DBSCAN clustering are well-converged, constructing core-set models based on the Jarvis-Patrick clustering cannot be recommended. In a well-converged core-set model, the number of core sets is up to an order of magnitude smaller than the number of states in a conventional Markov state model with comparable approximation error. Moreover, using the density-based clustering one can extend the core-set method to systems which are not strongly metastable. This is important for the practical application of the core-set method because most biologically interesting systems are only marginally metastable. The key point is to perform a hierarchical density-based clustering while monitoring the structure of the metric matrix which appears in the core-set method. We test this approach on a molecular-dynamics simulation of a highly flexible 14-residue peptide. The resulting core-set models have a high spatial resolution and can distinguish between conformationally similar yet chemically different structures, such as register-shifted hairpin structures.

Proper motions in the VVV Survey: Results for more than 15 million stars across NGC 6544

NASA Astrophysics Data System (ADS)

Contreras Ramos, R.; Zoccali, M.; Rojas, F.; Rojas-Arriagada, A.; Gárate, M.; Huijse, P.; Gran, F.; Soto, M.; Valcarce, A. A. R.; Estévez, P. A.; Minniti, D.

2017-12-01

Context. In the last six years, the VISTA Variable in the Vía Láctea (VVV) survey mapped 562 sq. deg. across the bulge and southern disk of the Galaxy. However, a detailed study of these regions, which includes 36 globular clusters (GCs) and thousands of open clusters is by no means an easy challenge. High differential reddening and severe crowding along the line of sight makes highly hamper to reliably distinguish stars belonging to different populations and/or systems. Aims: The aim of this study is to separate stars that likely belong to the Galactic GC NGC 6544 from its surrounding field by means of proper motion (PM) techniques. Methods: This work was based upon a new astrometric reduction method optimized for images of the VVV survey. Results: PSF-fitting photometry over the six years baseline of the survey allowed us to obtain a mean precision of 0.51 mas yr-1, in each PM coordinate, for stars with Ks< 15 mag. In the area studied here, cluster stars separate very well from field stars, down to the main sequence turnoff and below, allowing us to derive for the first time the absolute PM of NGC 6544. Isochrone fitting on the clean and differential reddening corrected cluster color magnitude diagram yields an age of 11-13 Gyr, and metallicity [Fe/H] =-1.5 dex, in agreement with previous studies restricted to the cluster core. We were able to derive the cluster orbit assuming an axisymmetric model of the Galaxy and conclude that NGC 6544 is likely a halo GC. We have not detected tidal tail signatures associated to the cluster, but a remarkable elongation in the galactic center direction has been found. The precision achieved in the PM determination also allows us to separate bulge stars from foreground disk stars, enabling the kinematical selection of bona fide bulge stars across the whole survey area. Conclusions: Kinematical techniques are a fundamental step toward disentangling different stellar populations that overlap in a studied field. Our results show that VVV data is perfectly suitable for this kind of analysis. Based on observations taken with ESO telescopes at Paranal Observatory under programme IDs 179.B-2002.

Theory for electron transfer from a mixed-valence dimer with paramagnetic sites to a mononuclear acceptor

NASA Astrophysics Data System (ADS)

Bominaar, E. L.; Achim, C.; Borshch, S. A.

1999-06-01

Polynuclear transition-metal complexes, such as Fe-S clusters, are the prosthetic groups in a large number of metalloproteins and serve as temporary electron storage units in a number of important redox-based biological processes. Polynuclearity distinguishes clusters from mononuclear centers and confers upon them unique properties, such as spin ordering and the presence of thermally accessible excited spin states in clusters with paramagnetic sites, and fractional valencies in clusters of the mixed-valence type. In an earlier study we presented an effective-mode (EM) analysis of electron transfer from a binuclear mixed-valence donor with paramagnetic sites to a mononuclear acceptor which revealed that the cluster-specific attributes have an important impact on the kinetics of long-range electron transfer. In the present study, the validity of these results is tested in the framework of more detailed theories which we have termed the multimode semiclassical (SC) model and the quantum-mechanical (QM) model. It is found that the qualitative trends in the rate constant are the same in all treatments and that the semiclassical models provide a good approximation of the more rigorous quantum-mechanical description of electron transfer under physiologically relevant conditions. In particular, the present results corroborate the importance of electron transfer via excited spin states in reactions with a low driving force and justify the use of semiclassical theory in cases in which the QM model is computationally too demanding. We consider cases in which either one or two donor sites of a dimer are electronically coupled to the acceptor. In the case of multiconnectivity, the rate constant for electron transfer from a valence-delocalized (class-III) donor is nonadditive with respect to transfer from individual metal sites of the donor and undergoes an order-of-magnitude change by reversing the sign of the intradimer metal-metal resonance parameter (β). In the case of single connectivity, the rate constant for electron transfer from a valence-localized (class-II) donor can readily be tuned over several orders of magnitude by introducing differences in the electronic potentials at the two metal sites of the donor. These results indicate that theories of cluster-based electron transfer, in order to be realistic, need to consider both intrinsic electronic structure and extrinsic interactions of the cluster with the protein environment.