Spatial clustering analysis of the foot-and-mouth disease outbreaks in Mato Grosso do Sul state, Brazil - 2005

In the southern region of Mato Grosso do Sul state, Brazil, a foot-and-mouth disease (FMD) epidemic started in September 2005. A total of 33 outbreaks were detected and 33,741 FMD-susceptible animals were slaughtered and destroyed. There were no reports of FMD cases in other species than bovines. Based on the data of this epidemic, it was carried out an analysis using the K-function and it was observed spatial clustering of outbreaks within a range of 25km. This observation may be related to the dynamics of foot-and-mouth disease spread and to the measures undertaken to control the disease dissemination. The control measures were effective once the disease did not spread to farms more than 47 km apart from the initial outbreaks.

Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall`s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index. (C) 2008 Elsevier Inc. All rights reserved.

Identification of protein expression signatures in gastric carcinomas using clustering analysis

Background and Aim: The identification of gastric carcinomas (GC) has traditionally been based on histomorphology. Recently, DNA microarrays have successfully been used to identify tumors through clustering of the expression profiles. Random forest clustering is widely used for tissue microarrays and other immunohistochemical data, because it handles highly-skewed tumor marker expressions well, and weighs the contribution of each marker according to its relatedness with other tumor markers. In the present study, we e identified biologically- and clinically-meaningful groups of GC by hierarchical clustering analysis of immunohistochemical protein expression. Methods: We selected 28 proteins (p16, p27, p21, cyclin D1, cyclin A, cyclin B1, pRb, p53, c-met, c-erbB-2, vascular endothelial growth factor, transforming growth factor [TGF]-beta I, TGF-beta II, MutS homolog-2, bcl-2, bax, bak, bcl-x, adenomatous polyposis coli, clathrin, E-cadherin, beta-catenin, mucin (MUC) 1, MUC2, MUC5AC, MUC6, matrix metalloproteinase [ MMP]-2, and MMP-9) to be investigated by immunohistochemistry in 482 GC. The analyses of the data were done using a random forest-clustering method. Results: Proteins related to cell cycle, growth factor, cell motility, cell adhesion, apoptosis, and matrix remodeling were highly expressed in GC. We identified protein expressions associated with poor survival in diffuse-type GC. Conclusions: Based on the expression analysis of 28 proteins, we identified two groups of GC that could not be explained by any clinicopathological variables, and a subgroup of long-surviving diffuse-type GC patients with a distinct molecular profile. These results provide not only a new molecular basis for understanding the biological properties of GC, but also better prediction of survival than the classic pathological grouping.

Clustering of Schistosoma mansoni mRNA sequences and analysis of the most transcribed genes: implications in metabolism and biology of different developmental stages

The study of the Schistosoma mansoni genome, one of the etiologic agents of human schistosomiasis, is essential for a better understanding of the biology and development of this parasite. In order to get an overview of all S. mansoni catalogued gene sequences, we performed a clustering analysis of the parasite mRNA sequences available in public databases. This was made using softwares PHRAP and CAP3. The consensus sequences, generated after the alignment of cluster constituent sequences, allowed the identification by database homology searches of the most expressed genes in the worm. We analyzed these genes and looked for a correlation between their high expression and parasite metabolism and biology. We observed that the majority of these genes is related to the maintenance of basic cell functions, encoding genes whose products are related to the cytoskeleton, intracellular transport and energy metabolism. Evidences are presented here that genes for aerobic energy metabolism are expressed in all the developmental stages analyzed. Some of the most expressed genes could not be identified by homology searches and may have some specific functions in the parasite.

Analysis and visualization of seismic data using mutual information

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

Pulmonary tuberculosis in Continental Portugal 2000-2010: temporal trends clustering as a tool for control evaluation

Clustering analysis is a useful tool to detect and monitor disease patterns and, consequently, to contribute for an effective population disease management. Portugal has the highest incidence of tuberculosis in the European Union (in 2012, 21.6 cases per 100.000 inhabitants), although it has been decreasing consistently. Two critical PTB (Pulmonary Tuberculosis) areas, metropolitan Oporto and metropolitan Lisbon regions, were previously identified through spatial and space-time clustering for PTB incidence rate and risk factors. Identifying clusters of temporal trends can further elucidate policy makers about municipalities showing a faster or a slower TB control improvement.

Genetic analysis of phenoxyalkanoic acid degradation in Sphingomonas herbicidovorans MH.

Phenoxyalkanoic acid degradation is well studied in Beta- and Gammaproteobacteria, but the genetic background has not been elucidated so far in Alphaproteobacteria. We report the isolation of several genes involved in dichlor- and mecoprop degradation from the alphaproteobacterium Sphingomonas herbicidovorans MH and propose that the degradation proceeds analogously to that previously reported for 2,4-dichlorophenoxyacetic acid (2,4-D). Two genes for alpha-ketoglutarate-dependent dioxygenases, sdpA(MH) and rdpA(MH), were found, both of which were adjacent to sequences with potential insertion elements. Furthermore, a gene for a dichlorophenol hydroxylase (tfdB), a putative regulatory gene (cadR), two genes for dichlorocatechol 1,2-dioxygenases (dccA(I/II)), two for dienelactone hydrolases (dccD(I/II)), part of a gene for maleylacetate reductase (dccE), and one gene for a potential phenoxyalkanoic acid permease were isolated. In contrast to other 2,4-D degraders, the sdp, rdp, and dcc genes were scattered over the genome and their expression was not tightly regulated. No coherent pattern was derived on the possible origin of the sdp, rdp, and dcc pathway genes. rdpA(MH) was 99% identical to rdpA(MC1), an (R)-dichlorprop/alpha-ketoglutarate dioxygenase from Delftia acidovorans MC1, which is evidence for a recent gene exchange between Alpha- and Betaproteobacteria. Conversely, DccA(I) and DccA(II) did not group within the known chlorocatechol 1,2-dioxygenases, but formed a separate branch in clustering analysis. This suggests a different reservoir and reduced transfer for the genes of the modified ortho-cleavage pathway in Alphaproteobacteria compared with the ones in Beta- and Gammaproteobacteria.

Proteomic analysis of mononuclear cells of patients with minimal-change nephrotic syndrome of childhood.

Background/Aims. Recently, peripheral blood mononuclear cell transcriptome analysis has identified genes that are upregulated in relapsing minimal-change nephrotic syndrome (MCNS). In order to investigate protein expression in peripheral blood mononuclear cells (PBMC) from relapsing MCNS patients, we performed proteomic comparisons of PBMC from patients with MCNS in relapse and controls. METHODS: PBMC from a total of 20 patients were analysed. PBMC were taken from five patients with relapsing MCNS, four in remission, five patients with other glomerular diseases and six controls. Two dimensional electrophoresis was performed and proteome patterns were compared. RESULTS: Automatic heuristic clustering analysis allowed us to pool correctly the gels from the MCNS patients in the relapse and in the control groups. Using hierarchical population matching, nine spots were found to be increased in PBMC from MCNS patients in relapse. Four spots were identified by mass spectrometry. Three of the four proteins identified (L-plastin, alpha-tropomyosin and annexin III) were cytoskeletal-associated proteins. Using western blot and immunochemistry, L-plastin and alpha-tropomyosin 3 concentrations were found to be enhanced in PBMC from MCNS patients in relapse. Conclusions. These data indicate that a specific proteomic profile characterizes PBMC from MCNS patients in relapse. Proteins involved in PBMC cytoskeletal rearrangement are increased in relapsing MCNS. We hypothesize that T-cell cytoskeletal rearrangement may play a role in the pathogenesis of MCNS by altering the expression of cell surface receptors and by modifying the interaction of these cells with glomerular cells.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

A comparative floristic analysis of a cerrado area in Botucatu, State of São Paulo (Brazil)

Based on the results of a phytosociological survey in a ''cerrado'' area located within the municipality of Botucatu, state of São Paulo Brazil, a comparative analysis of the 58 sampled tree species' is here presented using the data of other available floristics works for the ''cerrados in the same state. With a binary matrix of presence/absence of shared common species with the studied plot, a clustering analysis was performed thus obtaining a dendrogram which groups northern and southern areas with the same vegetation in the state of São Paulo previously treated as floristicaly differentiated. These results are discussed under the assumption that Isolated areas of ''cerrados'' are to be taken as targets for immediate conservation.

A comparative transcriptome analysis reveals expression profiles conserved across three Eimeria spp. of domestic fowl and associated with multiple developmental stages

Coccidiosis of the domestic fowl is a worldwide disease caused by seven species of protozoan parasites of the genus Eimeria. The genome of the model species, Eimeria tenella, presents a complexity of 55-60 MB distributed in 14 chromosomes. Relatively few studies have been undertaken to unravel the complexity of the transcriptome of Eimeria parasites. We report here the generation of more than 45,000 open reading frame expressed sequence tag (ORESTES) cDNA reads of E. tenella, Eimeria maxima and Eimeria acervulina, covering several developmental stages: unsporulated oocysts, sporoblastic oocysts, sporulated oocysts, sporozoites and second generation merozoites. All reads were assembled to constitute gene indices and submitted to a comprehensive functional annotation pipeline. In the case of E. tenella, we also incorporated publicly available ESTs to generate an integrated body of information. Orthology analyses have identified genes conserved across different apicomplexan parasites, as well as genes restricted to the genus Eimeria. Digital expression profiles obtained from ORESTES/EST countings, submitted to clustering analyses, revealed a high conservation pattern across the three Eimeria spp. Distance trees showed that unsporulated and sporoblastic oocysts constitute a distinct clade in all species, with sporulated oocysts forming a more external branch. This latter stage also shows a close relationship with sporozoites, whereas first and second generation merozoites are more closely related to each other than to sporozoites. The profiles were unambiguously associated with the distinct developmental stages and strongly correlated with the order of the stages in the parasite life cycle. Finally, we present The Eimeria Transcript Database (http://www.coccidia.icb.usp.br/eimeriatdb), a website that provides open access to all sequencing data, annotation and comparative analysis. We expect this repository to represent a useful resource to the Eimeria scientific community, helping to define potential candidates for the development of new strategies to control coccidiosis of the domestic fowl. (C) 2011 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.