Genetic diversity among Curtobacterium flaccumfaciens pv. flaccumfaciens populations in the American High Plains

2012 ◽  
Vol 58 (6) ◽  
pp. 788-801 ◽  
Author(s):  
I.V. Agarkova ◽  
P.A. Lambrecht ◽  
A.K. Vidaver ◽  
R.M. Harveson
Keyword(s):  
Cluster Analysis ◽  
Population Heterogeneity ◽  
High Plains ◽  
Positive Bacterium ◽  
Data Set ◽  
Yellow Orange ◽  
Polymerase Chain ◽  
Extracellular Pigment ◽  
Composite Data ◽  
Color Variants

Curtobacterium flaccumfaciens pv. flaccumfaciens is a Gram-positive bacterium and has reemerged as an incitant of bacterial wilt in common (dry, edible) beans in western Nebraska, eastern Colorado, and southeastern Wyoming. Curtobacterium flaccumfaciens pv. flaccumfaciens is diverse phenotypically and genotypically and is represented by several different pathogen color variants. The population structure of 67 strains collected between 1957 and 2009, including some isolated from alternate hosts, was determined with 3 molecular typing techniques: amplified fragment length polymorphism (AFLP), repetitive extragenic palindromic polymerase chain reaction (rep-PCR), and pulsed-field gel electrophoresis (PFGE). All 3 typing techniques showed a great degree of population heterogeneity, but they were not congruent in cluster analysis of the C. flaccumfaciens pv. flaccumfaciens populations. Cluster analysis of a composite data set (AFLP, PFGE, and rep-PCR) using averages from all experiments yielded 2 distinct groups: cluster A included strains with colonies of yellow, orange, and pink pigments, and cluster B had strains of only yellow pigment. Strains producing purple extracellular pigment were assigned to both clusters. Thus, C. flaccumfaciens pv. flaccumfaciens is diverse phenotypically and genotypically.

scholarly journals Spatial pattern and genetic diversity estimates are linked in stochastic models of population differentiation

2000 ◽  
Vol 23 (3) ◽  
pp. 541-544 ◽  
Author(s):  
José Alexandre Felizola Diniz-Filho ◽  
Mariana Pires de Campos Telles
Keyword(s):  
Genetic Diversity ◽  
Spatial Pattern ◽  
Stochastic Models ◽  
Population Differentiation ◽  
Real Data ◽  
Population Heterogeneity ◽  
Data Set ◽  
Mantel’S Test ◽  
The Relationship ◽  
Diversity Estimates

In the present study, we used both simulations and real data set analyses to show that, under stochastic processes of population differentiation, the concepts of spatial heterogeneity and spatial pattern overlap. In these processes, the proportion of variation among and within a population (measured by G ST and 1 - G ST, respectively) is correlated with the slope and intercept of a Mantel's test relating genetic and geographic distances. Beyond the conceptual interest, the inspection of the relationship between population heterogeneity and spatial pattern can be used to test departures from stochasticity in the study of population differentiation.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals Molecular typing of Clostridium perfringens isolated from swine in slaughterhouses from São Paulo State, Brazil

2012 ◽  
Vol 42 (8) ◽  
pp. 1450-1456 ◽  
Author(s):  
Thais Sebastiana Porfida Ferreira ◽  
Andrea Micke Moreno ◽  
Renata Rodrigues de Almeida ◽  
Cleise Ribeiro Gomes ◽  
Debora Dirani Sena de Gobbi ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Clostridium Perfringens ◽  
Food Poisoning ◽  
Positive Bacterium ◽  
Chain Reaction ◽  
High Prevalence ◽  
Fecal Isolate ◽  
Finishing Pig ◽  
Type C ◽  
Polymerase Chain

Clostridium perfringens is an anaerobic Gram-positive bacterium known as common pathogen for humans, for domestic and wildlife animals. Although infections caused by C. perfringens type C and A in swine are well studied, just a few reports describe the genetic relationship among strains in the epidemiological chain of swine clostridioses, as well as the presence of the microorganism in the slaughterhouses. The aim of the present study was to isolate C. perfringens from feces and carcasses from swine slaughterhouses, characterize the strains in relation to the presence of enterotoxin, alpha, beta, epsilon, iota and beta-2 toxins genes, using polymerase chain reaction (PCR) and comparing strains by means of Pulsed field gel electrophoresis (PFGE). Clostridium perfringens isolation frequencies in carcasses and finishing pig intestines were of 58.8% in both types of samples. According to the polymerase chain reaction assay, only alfa toxin was detected, being all isolates also negative to enterotoxin and beta2 toxin. Through PFGE technique, the strains were characterized in 35 pulsotypes. In only one pulsotype, the isolate from carcass sample was grouped with fecal isolate of the same animal, suggesting that the risk of cross-contamination was low. Despite the high prevalence of C. perfringens in swine carcasses from the slaughterhouses assessed, the risk of food poisoning to Brazilian pork consumers is low, since all strains were negative to cpe-gene, codifying enterotoxin.

scholarly journals Cluster analysis of the results of intraoperative optical spectroscopic diagnostics In brain glioma neurosurgery

2019 ◽  
Vol 7 (4) ◽  
pp. 23-34
Author(s):  
I. A. Osmakov ◽  
T. A. Savelieva ◽  
V. B. Loschenov ◽  
S. A. Goryajnov ◽  
A. A. Potapov
Keyword(s):  
Cluster Analysis ◽  
Optical Spectroscopy ◽  
Protoporphyrin Ix ◽  
Spectroscopy Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Broadband Radiation ◽  
Degree Of Malignancy ◽  
Normal White Matter

The paper presents the results of a comparative study of methods of cluster analysis of optical intraoperative spectroscopy data during surgery of glial tumors with varying degree of malignancy. The analysis was carried out both for individual patients and for the entire dataset. The data were obtained using combined optical spectroscopy technique, which allowed simultaneous registration of diffuse reflectance spectra of broadband radiation in the 500–600 nm spectral range (for the analysis of tissue blood supply and the degree of hemoglobin oxygenation), fluorescence spectra of 5‑ALA induced protoporphyrin IX (Pp IX) (for analysis of the malignancy degree) and signal of diffusely reflected laser light used to excite Pp IX fluorescence (to take into account the scattering properties of tissues). To determine the threshold values of these parameters for the tumor, the infltration zone and the normal white matter, we searched for the natural clusters in the available intraoperative optical spectroscopy data and compared them with the results of the pathomorphology. It was shown that, among the considered clustering methods, EM‑algorithm and k‑means methods are optimal for the considered data set and can be used to build a decision support system (DSS) for spectroscopic intraoperative navigation in neurosurgery. Results of clustering relevant to thepathological studies were also obtained using the methods of spectral and agglomerative clustering. These methods can be used to postprocess combined spectroscopy data.

scholarly journals Population Heterogeneity of Lactobacillus plantarum WCFS1 Microcolonies in Response to and Recovery from Acid Stress

2008 ◽  
Vol 74 (24) ◽  
pp. 7750-7758 ◽  
Author(s):  
Colin J. Ingham ◽  
Marke Beerthuyzen ◽  
Johan van Hylckama Vlieg
Keyword(s):  
Lactobacillus Plantarum ◽  
Porous Ceramic ◽  
Acid Stress ◽  
Low Ph ◽  
Phenotypic Heterogeneity ◽  
Population Heterogeneity ◽  
Positive Bacterium ◽  
Ceramic Support ◽  
Dividing Cells ◽  
Stressed Cells

ABSTRACT Within an isogenic microbial population in a homogenous environment, individual bacteria can still exhibit differences in phenotype. Phenotypic heterogeneity can facilitate the survival of subpopulations under stress. As the gram-positive bacterium Lactobacillus plantarum grows, it acidifies the growth medium to a low pH. We have examined the growth of L. plantarum microcolonies after rapid pH downshift (pH 2 to 4), which prevents growth in liquid culture. This acidification was achieved by transferring cells from liquid broth onto a porous ceramic support, placed on a base of low-pH MRS medium solidified using Gelrite. We found a subpopulation of cells that displayed phenotypic heterogeneity and continued to grow at pH 3, which resulted in microcolonies dominated by viable but elongated (filamentous) cells lacking septation, as determined by scanning electron microscopy and staining cell membranes with the lipophilic dye FM4-64. Recovery of pH-stressed cells from these colonies was studied by inoculation onto MRS-Gelrite-covered slides at pH 6.5, and outgrowth was monitored by microscopy. The heterogeneity of the population, calculated from the microcolony areas, decreased with recovery from pH 3 over a period of a few hours. Filamentous cells did not have an advantage in outgrowth during recovery. Specific regions within single filamentous cells were more able to form rapidly dividing cells, i.e., there was heterogeneity even within single recovering cells.

Exploring the Unknown Nature of Data

2010 ◽  
pp. 1-27
Author(s):  
Rui Xu ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Data Structures ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Human Beings ◽  
Data Set ◽  
Learning Capabilities ◽  
Good Learning ◽  
Hidden Data

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

scholarly journals The “Super-Child” Approach Is Applied To Estimate Retinol Kinetics and Vitamin A Total Body Stores in Mexican Preschoolers

2020 ◽  
Vol 150 (6) ◽  
pp. 1644-1651 ◽  
Author(s):  
Veronica Lopez-Teros ◽  
Jennifer L Ford ◽  
Michael H Green ◽  
Brianda Monreal-Barraza ◽  
Lilian García-Miranda ◽  
...  
Keyword(s):  
Vitamin A ◽  
Corn Oil ◽  
Compartmental Analysis ◽  
Whole Body ◽  
Retinyl Acetate ◽  
Data Set ◽  
Mexican Children ◽  
Model Predictions ◽  
Total Body ◽  
Composite Data

ABSTRACT Background Retinol isotope dilution (RID) and model-based compartmental analysis are recognized techniques for assessing vitamin A (VA) status. Recent studies have shown that RID predictions of VA total body stores (TBS) can be improved by using modeling and that VA kinetics and TBS in children can be effectively studied by applying population modeling (“super-child” approach) to a composite data set. Objectives The objectives were to model whole-body retinol kinetics and predict VA TBS in a group of Mexican preschoolers using the super-child approach and to use model predictions of RID coefficients to estimate TBS by RID in individuals. Methods Twenty-four healthy Mexican children (aged 3–6 y) received an oral dose (2.96 μmol) of [13C10]retinyl acetate in corn oil. Blood samples were collected from 8 h to 21 d after dosing, with each child sampled at 4 d and at 1 other time. Composite data for plasma labeled retinol compared with time were analyzed using a 6-component model to obtain group retinol kinetic parameters and pool sizes. Model-predicted TBS was compared with mean RID predictions at 4 d; RID estimates at 4 d were compared with those calculated at 7–21 d. Results Model-predicted TBS was 1097 μmol, equivalent to ∼2.4 y-worth of VA; using model-derived coefficients, group mean RID-predicted TBS was 1096 μmol (IQR: 836–1492 μmol). TBS at 4 d compared with a later time was similar (P = 0.33). The model predicted that retinol spent 1.5 h in plasma during each transit and recycled to plasma 13 times before utilization. Conclusions The super-child modeling approach provides information on whole-body VA kinetics and can be used with RID to estimate TBS at any time between 4 and 21 d postdose. The high TBS predicted for these children suggests positive VA balance, likely due to large-dose VA supplements, and warrants further investigation.

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