Search results for "cluster analysis"
showing 10 items of 848 documents
Multivariate approaches to behavioral physiology
2017
A systematic comparison of kinetic modelling methods generating parametric maps for [11C]-(R)-PK11195
2006
[(11)C]-(R)-PK11195 is presently the most widely used radiotracer for the monitoring of microglia activity in the central nervous system (CNS). Microglia, the resident immune cells of the brain, play a critical role in acute and chronic diseases of the central nervous system and in host defence against neoplasia. The purpose of this investigation was to evaluate the reliability and sensitivity of five kinetic modelling methods for the formation of parametric maps from dynamic [(11)C]-(R)-PK11195 studies. The methods we tested were the simplified reference tissue model (SRTM), basis pursuit, a simple target-to-reference ratio, the Logan plot and a wavelet based Logan plot. For the reliabilit…
Molecular Typing Reveals Frequent Clustering among Human Isolates of Listeria monocytogenes in Italy
2009
In Italy, the annual incidence of reported cases of listeriosis amounts in recent years (2004 to 2006) to 0.8 cases per million inhabitants. Our study is a subtyping analysis by serotyping, ribotyping, and pulsed-field gel electrophoresis analysis of 44 human isolates from apparently sporadic cases of infection in the Lombardy region and in the Province of Florence, Italy, in the years 1996 to 2007. Based on the results of the different subtyping methods, 10 occasions were detected when strains of L. monocytogenes with the same subtype were isolated from more than one listeriosis case. A total of 28 (66.7%) out of 44 isolates were attributed to molecular subtype clusters. Our data support t…
Vigna mungo, V. radiata and V. unguiculata plants sampled in different agronomical-ecological-climatic regions of India are nodulated by Bradyrhizobi…
2009
International audience; Vigna mungo, Vigna radiata and Vigna unguiculata are important legume crops cultivated in India, but little is known about the genetic resources in native rhizobia that nodulate these species. To identify these bacteria, a core collection of 76 slow-growing isolates was built from root nodules of V. mungo, V. radiata and V. unguiculata plants grown at different sites within three agro-ecological-climatic regions of India. The genetic diversity of the bacterial collection was assessed by restriction fragment length polymorphism (RFLP) analysis of PCR-amplified DNA fragments of the 16S–23S rDNA intergenic spacer (IGS) region, and the symbiotic genes nifH and nodC. One …
Weighted Least-Squares Likelihood Ratio Test for Branch Testing in Phylogenies Reconstructed from Distance Measures
2005
A variety of analytical methods is available for branch testing in distance-based phylogenies. However, these methods are rarely used, possibly because the estimation of some of their statistics, especially the covariances, is not always feasible. We show that these difficulties can be overcome if some simplifying assumptions are made, namely distance independence. The weighted least-squares likelihood ratio test (WLS-LRT) we propose is easy to perform, using only the distances and some of their associated variances. If no variances are known, the use of the Felsenstein F-test, also based on weighted least squares, is discussed. Using simulated data and a data set of 43 mammalian mitochondr…
Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)
2021
Forest fires are undesirable situations with tremendous impacts on wildlife and people&rsquo
RabbitMash: accelerating hash-based genome analysis on modern multi-core architectures
2020
Abstract Motivation Mash is a popular hash-based genome analysis toolkit with applications to important downstream analyses tasks such as clustering and assembly. However, Mash is currently not able to fully exploit the capabilities of modern multi-core architectures, which in turn leads to high runtimes for large-scale genomic datasets. Results We present RabbitMash, an efficient highly optimized implementation of Mash which can take full advantage of modern hardware including multi-threading, vectorization and fast I/O. We show that our approach achieves speedups of at least 1.3, 9.8, 8.5 and 4.4 compared to Mash for the operations sketch, dist, triangle and screen, respectively. Furtherm…
Gravitational weighted fuzzy c-means with application on multispectral image segmentation
2014
This paper presents a novel clustering approach based on the classic Fuzzy c-means algorithm. The approach is inspired from the concept of interaction between objects in physics. Each data point is regarded as a particle. A specific weight is associated with each data particle depending on its interaction with other particles. This interaction is induced by attraction forces between pairs of particles and the escape velocity from other particles. Classification experiments using two data sets from UCI repository demonstrate the outperformance of the proposed approach over other clustering algorithms. In addition, results demonstrate the effectiveness of the proposed scheme for segmentation …
Clustering categorical data: A stability analysis framework
2011
Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation …
A Clustering Approach to texture Classification
1988
In the paper a clustering technique to segment an image in to “homogeneous” regions is studied. The homogeneity of each region is evaluated by means of a “proximity function” computed between the pixels. The main result of such approach is that no-histogramming is required in order to perform segmentation. Possibilistic and probabilistic approaches are, also, combined to evaluate the significativity of the computed regions.