Fast Clustering Analysis Research Articles

Rapid typing of Klebsiella pneumoniae and Pseudomonas aeruginosa by Fourier-transform Infrared spectroscopy informs infection control in veterinary settings.

The emergence of multi-drug resistant (MDR) pathogens linked to healthcare-associated infections (HCAIs) is an increasing concern in modern veterinary practice. Thus, rapid bacterial typing for real-time tracking of MDR hospital dissemination is still much needed to inform best infection control practices in a clinically relevant timeframe. To this end, the IR Biotyper using Fourier-Transform InfraRed (FTIR) spectroscopy has the potential to provide fast cluster analysis of potentially related organisms with substantial cost and turnaround time benefits. A collection of MDR bacterial isolates (n = 199, comprising 92 Klebsiella pneumoniae and 107 Pseudomonas aeruginosa) obtained from companion animal (i.e., dogs, cats and horses) clinical investigations, faecal and environmental screening from four veterinary facilities between 2012 and 2019 was analysed retrospectively by FTIR spectroscopy. Its performance was compared against MLST extracted from whole genomes of a subset of clustering isolates (proportionally to cluster size) for investigation of potential nosocomial transmission between patients and the surrounding hospital environments. Concordance between the FTIR and MLST types was overall high for K. pneumoniae (Adjusted Rand Index [ARI] of 0.958) and poor for P. aeruginosa (ARI of 0.313). FTIR K. pneumoniae clusters (n = 7) accurately segregated into their respective veterinary facility with evidence of intra-hospital spread of K. pneumoniae between patients and environmental surfaces. Notably, K. pneumoniae ST147 intensely circulated at one Small Animal Hospital ICU. Conversely, Pseudomonas aeruginosa FTIR clusters (n = 18) commonly contained isolates of diversified hospital source and heterogeneous genetic background (as also genetically related isolates spread across different clusters); nonetheless, dissemination of some clones, such as P. aeruginosa ST2644 in the equine hospital, was apparent. Importantly, FTIR clustering of clinical, colonisation and/or environmental isolates sharing genomically similar backgrounds was seen for both MDR organisms, highlighting likely cross-contamination events that led to clonal dissemination within settings. FTIR spectroscopy has high discriminatory power for hospital epidemiological surveillance of veterinary K. pneumoniae and could provide sufficient information to support early detection of clonal dissemination, facilitating implementation of appropriate infection control measures. Further work and careful optimisation need to be carried out to improve its performance for typing of P. aeruginosa veterinary isolates.

RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.0 TB in FASTA format, within only 34 min on a 128-core workstation. Our results further identify 1269 redundant genomes, with identical nucleotide content, in the RefSeq bacterial genomes database.

Statistical Characteristics of Urban Changzhou PM2.5 Based on k-means Analysis

Statistical analysis methods was utilized to investigate the variations of monthly average concentrations of the six basic pollutants (SO2, NO2, CO, O3, PM2.5 and PM10) of six national standard monitoring sites from 2013 to 2014 in urban Changzhou. The results showed that, except for O3, SO2, NO2, CO, PM2.5 and PM10 concentrations were all high in winter and low in summer. The relationship between particulate matter and wind speed showed, with increasing wind speed, the concentration of PM2.5 reduced. However, the concentration variations of PM10 were complicated and when wind speed increased, its concentration started to go down and then elevated. Fast-cluster analysis (k-means) and the index of SWV & DIV were used to classify the six basic pollutants into four clusters, and then the relationship between gaseous pollutants and PM2.5in each cluster was emphatically discussed by statistical analysis method. Four clusters were assigned to fossil fuel combustion emissions (cluster1), O3 and secondary aerosols (cluster2), incomplete combustion emissions and regional haze (cluster3), urban city "background" (cluster4). Incomplete combustion cluster accounted for the smallest percentage of urban Changzhou and city "background" was cluster of urban Changzhou with the largest contribution. k-means analysis results also showed that PM2.5 had complex sources in urban Changzhou.

Wireless channel extraction analyzing based on graph theory

Wireless channels comprise various signal characteristics that correspond to different features. This research applies digital signal processing to first excavate and categorize various features found in the channel data. Then, borrowing from graph theory, fast clustering analysis and decision tree modeling are introduced to identify unique “fingerprint” characteristics. Finally, two scenarios were tested using artificial neural networks to identify and verify their applicability in different geographical locations.

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

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

Screening the USDA Watermelon Germplasm Collection for Drought Tolerance at the Seedling Stage

Because of the growing threat of global warming, drought stress could severely affect the normal growth and development of crop plants. To alleviate such an adverse effect, there is a need to screen watermelon germplasm collections to identify genetic sources for potential drought tolerance. In the present study, 820 accessions of USDA's Citrullus PIs and 246 watermelon breeding lines were evaluated for their drought tolerance at the seedling stage under extreme water stress conditions in a greenhouse. Significant variations in drought tolerance were observed in the Citrullus germplasm collections. Using fast clustering analysis, the tested watermelon materials could be assigned into four groups, including tolerant, intermediate tolerant, moderately sensitive, and sensitive, respectively. The most drought-tolerant Citrullus germplasm, including 13 Citrullus lanatus var. lanatus and 12 C. lanatus var. citroides accessions, were originated from Africa. These genetic materials could be used for rootstock breeding or for developing drought-tolerant watermelon cultivars.

A Cluster-Analysis-Based Efficient Multibaseline Phase-Unwrapping Algorithm

Multibaseline phase unwrapping is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). It has an advantage over single-baseline phase unwrapping in discontinuous-terrain-height estimation. In this paper, the pixels' combination information of multiple InSAR interferograms with different baseline lengths is deeply investigated, and the term ambiguity vector is proposed to represent a pixel's ambiguity number of multiple interferograms. It is also revealed that pixels with the same ambiguity vector have an exclusive pattern among them. A fast cluster-analysis (CA)-based method is proposed for multibaseline phase unwrapping. In this method, all pixels are first clustered into different groups according to their patterns, and then, information of the cluster center is used to unwrap the phases of pixels group by group. Simulation results are shown to verify the effectiveness, efficiency, and noise robustness of CA multibaseline phase-unwrapping method.

Using Similarity Analyses to Scale Out Research Findings Across Andean Watershed Basins

Strategic research in agriculture and natural resources carried out by international research centers is deemed a public good and should, sooner or later, be put into the hands of development, governmental and non-governmental organizations. However, this research is usually done at specific pilot sites; there is a greater need to know how representative those sites are in relation to the diversity of contexts in other locations. Such is the case with the Challenge Program on Water and Food (CPWF), a global initiative in water research promoted by the Consultative Group on International Agricultural Research (CGIAR), which is developing and implementing strategic research in nine basins located in the tropics of Africa, Asia and South America. Given that resources are not available to collect data from the whole of the region, pilot sites are needed. It is hoped that research outputs obtained in the selected pilot sites can be the basis for scaling out solutions to similar situations in neighbouring or adjacent areas in same or different basins. In order to contribute to the scaling-out process, different classification methodologies were applied to determine how specific watershed basins are representative of larger areas. The Andean eco-region served as a case study but the methods can easily be applied in other regions. The spatial diversity of biophysical and social conditions across the Andes requires careful site selection. Two methods, a combination of Weight of Evidence (WofE) and Logistic Regression (LR) methods and Fast Cluster analysis, were used to determine the similarity of selected sites with those excluded. A 1-km study resolution covering most of the Andes eco-region included annual rainfall, elevation, length of growing period, land cover, roads and population density as the key variables. Results showed complementarities between the two methods in presenting a probability surface of similarity across the Andes and a clustering of similar sites inside and outside the pilot basins. The output information forms a strong basis for devising plans to scale out research findings from the pilot basins to the whole region.

Peer influence groups: identifying dense clusters in large networks

Sociologists have seen a dramatic increase in the size and availability of social network data. This represents a poverty of riches, however, since many of our analysis techniques cannot handle the resulting large (tens to hundreds of thousands of nodes) networks. In this paper, I provide a method for identifying dense regions within large networks based on a peer influence model. Using software familiar to most sociologists, the method reduces the network to a set of m position variables that can then be used in fast cluster analysis programs. The method is tested against simulated networks with a known small-world structure showing that the underlying clusters can be accurately recovered. I then compare the performance of the procedure with other subgroup detection algorithms on the MacRea and Gagnon prison friendship data and a larger adolescent friendship network, showing that the algorithm replicates other procedures for small networks and outperforms them on the larger friendship network.