Numerical classification and ordination of ruderal plant communities (Sisymbrietalia,Onopordetalia) in the western part of Slovakia

The ruderal communities of the orders Sisymbrietalia and Onopordetalia from the western part of Slovakia have been subjected to numerical classification and ordination. The ordination techniques proved to be a useful tool in the elucidation of the cluster pattern as well as in the detection of the main environmental variation underlying the floristic variation within the data. Results obtained with numerical techniques and traditional syntaxonomical classification have been compared. The similarity between these results is low at the level of the orders. This incompatability is explained by the differences in the weighting of the species in the course of the classification process and by the addition of non-floristical criteria that often occurs in syntaxonomical classification according to Braun-Blanquet. The highest value has been observed at the 3-clusters level (both orders and the Malvion neglectae). High similarity among the results of the numerical techniques have been observed, particularly in the group of space-dilating clusterings (Ward's method, Complete linkage clustering and MeQuitty's similarity analysis). Average linkage clustering produces the most diverse result. The Malvion neglectae appeared as a separate group in all numerical techniques adopted. This suggests the upranking of its syntaxonomical position. The Bromo-Hordeion murini turned out to be a very heterotoneous syntaxon.     

Discovering Study-Specific Gene Regulatory Networks

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method''s results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.     

Insights from the clustering of microarray data associated with the heart disease

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines'' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.     

A Polychromatic Stain for Use in Parasitology

In sensory systems, insight into synaptic arrangements on cells of known physiological response properties has helped our understanding of the structural basis for these properties. To carry out these types of studies, however, synaptic types in the region of interest must be defined. Unfortunately, defining synaptic types in the brainstem has proved to be a challenging enterprise. Our study was done to classify synapses in the gustatory part of the nucleus solitarius using objective quantitative criteria and a cluster analysis procedure. Cluster analysis allows classification of a population of objects, such as synaptic terminals, into groups that exhibit similar characteristics. Six terminal types were identified using cluster analysis and subsequent analyses of variance and post hoc tests. Unlike classification schemes used for the cerebral cortex, where synaptic apposition density thickness and shape of vesicles is useful (Gray's Type I and II synapses), the concentration of vesicles in a terminal was a more useful measurement with which to classify terminals in the nucleus solitarius. To validate that vesicle density (vesicles/μm²) is a useful defining characteristic to classify terminals in the nucleus solitarius, terminals of a known type were used. GABAergic terminals were identified using postembedding immunohistochemical techniques, and their vesicle density was determined. GABAergic terminals fall into the range of two of the terminal types defined by the cluster analysis and, based on vesicle density, two types of GABAergic terminals were identified. We conclude that vesicle density is a helpful means to identify synapses in this brainstem nucleus.     

Dermatoglyphic assessment of the genetic relationships of native American populations

Genetic distance techniques and cluster analysis were used to determine which dermatoglyphic parameters were most useful in assessing genetic relationships between native American populations. The dermatoglyphic parameters used for this analysis were those most completely reported in the papers examined in a literature survey we had carried out previously, and include digital patterns, modal types of the C-line and D-line terminations, and palmar patterns in the hypothenar, thenar/I, and interdigital areas. The populations examined were separated by sex and divided into seven major geographical groups: Eskimo; North American Indian; Maya and non-Maya (Central America); Amazonian; Quechua, and Aymara (South America). Mean frequencies of dermatoglyphic traits were computed for each group and pairwise “genetic” distances calculated using Cavalli-Sforza and Edward's D-test. Dendrograms were then generated from these D-values using Q-mode cluster analysis. Using the hypothenar, thenar/I and III and IV interdigital areas, the results of this analysis demonstrated a good fit to an expected phylogenetic tree. When digital patterns, patterns in the II interdigital area, and the modal types of the C-line were included in the analysis, the same phylogenetic relationships were observed. However, inclusion of the modal types of the D-line with the other parameters tended to introduce some distortion and a greater separation between sexes within the same populations.     

On the determination of optimal levels in phytosociological classification

A method is introduced to compare results of a clustering technique at different levels of abstraction, or of different clustering techniques. The method emphasizes within cluster homogeneity as well as discontinuities between clusters. It has been derived from Hogeweg's method with some important changes. First each cluster is handled separately to determine the ratio between homogeneity and similarity to the nearest neighbour cluster. For a given clustering a weighted average value is computed over all clusters. This average value is standardized using an expected average value for a cluster configuration with the same number of clusters having the same sizes. A low level of the ratio between expected and observed values is supposed to indicate an optimal clustering. A derivation of the criterion is given and results from three sets of data with different properties are evaluated.     

Comparison of algorithms for prediction of related proteins using the method of phylogenetic profiles

Computational interactomics deals with prediction of functionally related proteins. One approach for solving this problem using comparative genomics consists in analysis of similarities between phylogenetic profiles of proteins. In contrast to most methods, which predict only pairwise interactions between proteins, in the present work we have applied the cluster analysis techniques in order to find modules of functionally related proteins. We have performed the cluster analysis of phylogenetic profiles of E. coli proteins using several clustering techniques and various modes for estimation of distances between profiles. We report here, that the best correspondence in the composition of resultant clusters to known metabolic pathways is achieved using Ward’s clustering together with Hamming’s distance. The proposed technique of assessing predictions of the modules of functionally related proteins can be used for comparative analysis of different algorithms for computational interactomics.     

Characterizing heterogeneity of non‐small cell lung tumour microenvironment to identify signature prognostic genes

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non‐small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 (''low infiltration'' cluster), cluster 2 (''heterogeneous infiltration'' cluster) and cluster 3 (''high infiltration'' cluster). The immune cells were responsible for a significantly favourable prognosis for the ''high infiltration'' community. Cluster 1 had the lowest cytotoxic activity, tumour‐infiltrating lymphocytes and interferon‐gamma (IFN‐γ), as well as immune checkpoint molecules expressions. In addition, MHC‐I and immune co‐stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune‐related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.     

Multivariate analysis of anthropometric data and classifications of British Columbian natives

Anthropometric data collected in native populations of British Columbia in the late 19th century by Franz Boas were analyzed by two multivariate techniques. Multivariate analysis of variance was used to test physical classificatory units devised by Boas and an ad hoc classification based on local cultural units. Both were found to have some empirical validity. Mahalanobis' D (Mahalanobis, '30) was computed between pairs of local groups, for both sexes. From these a matrix of differences was prepared and diagrams drawn to illustrate phenetic relationships among samples. By this means one cluster of groups, Interior B.C. peoples, could be distinguished and other local samples appeared distinctly different from each other. It was concluded that in the absence of genealogical data by which to identify local populations, local cultural units are preferable to more inclusive units for making empirical comparisons and classifications.     

BIOACOUSTIC PUBLICATIONS, 1986

ABSTRACT

A common task for researchers of animal vocalisations is statistically comparing repertoires, or sets of vocalisations. We evaluated five methods of comparing repertoires of ‘codas’, short repeated patterns of clicks, recorded from sperm whale (Physeter macrocephalus) groups. Three of the methods involved classification of codas—human observer classification, k-means cluster analysis using Calinski and Harabasz's (1974) criterion to determine k, and a divisive k-means clustering procedure using Duda and Hart's (1973) criterion to determine k. Two other methods used multivariate distances to calculate similarity measures between coda repertoires. When used on a sample coda dataset, observer classification failed to produce consistent results. Calinski and Harabasz's criterion did not provide a clear signal for determining the number of coda classes (k). Divisive clustering using Duda and Hart's criterion performed satisfactorily and, encouragingly, gave similar results to the multivariate similarity measures when used on our data. However, the relative performance of the k-means techniques is likely data dependent, so one method is not likely to perform best in all circumstances. Thus results should be checked to ensure they extract logical clusters. Using these techniques concurrently with multivariate measures allows the drawing of relatively robust conclusions about repertoire similarity while minimising uncertainties due to questionable validity of classifications.     

六个品种花椰菜花球的营养成分分析与评价

为评价花椰菜(Brassica oleracea var. botrytis)的营养品质,测定了6个品种花球的10项营养指标,运用主成分分析和聚类分析方法对花椰菜品质进行综合评价。结果表明,6个品种的10项品质指标均存在不同程度的差异,变异幅度为12.22%~ 131.21%。维生素C (Vc)、总黄酮、总多酚、Fe、Ca、P、蛋白质含量间存在显著或极显著的关联性。系统聚类分析将6种花椰菜分为4类,黄色花椰菜‘209’、‘100’和‘217’各为1类,白色花椰菜‘210’、‘214’和‘218’聚为1类。主成分分析提取了花椰菜品质综合评价的3个主成分,获得6个营养评价指标：Vc、总黄酮、总多酚、Fe、Ca和P。通过建立评价函数模型: F= 0.5591Z₁+0.2189Z₂+0.1669Z₃,筛选出‘209’花椰菜的营养品质最高。这为挖掘及选育优良花椰菜品种提供了科学依据。     

Hospital cluster of HBV infection: molecular evidence of patient-to-patient transmission through lancing device

Introduction

In western countries the transmission of hepatitis B virus (HBV) transmission through multi-patients lancing devices has been inferred since early ‘90s, however no study has ever provided biological evidence which directly link these device with HBV cross-infection. Here we present results of an outbreak investigation which could associate, by molecular techniques, the use of lancing device on multiple patients with HBV transmission in an Italian oncohematology unit.

Methods

The outbreak investigation was designed as a retrospective cohort study to identify all potential cases. All cases identified were eventually confirmed through molecular epidemiology techniques. Audit of personnel including extensive review of infection control measures and reviewing personnel''s tests for HBV was done identify transmission route.

Results

Between 4 May 2006 and 21 February 2007, six incident cases of HBV infection were reported among 162 patients admitted in the oncohematology. The subsequent molecular instigation proved that 3 out 6 incident cases and one prevalent cases (already infected with HBV at the admission) represented a monophyletic cluster of infection. The eventual environmental investigation found that an identical HBV viral strain was present on a multi-patients lancing device in use in the unit and the inferential analysis showed a statistically significant association between undergoing lancing procedures and the infection.

Discussion

This investigation provide molecular evidence to link a HBV infection cluster to multi-patients lancing device and highlights that patients undergoing capillary blood sampling by non-disposable lancing device may face an unacceptable increased risk of HBV infection. Therefore we believe that multi-patients lancing devices should be banned from healthcare settings and replace with disposable safety lancets that permanently retract to prevent the use of the same device on multiple patients. The use of non-disposable lancing devices should be restricted to individual use at patients'' home.     

Improved MPI collectives for MPI processes in shared address spaces

As the number of cores per node keeps increasing, it becomes increasingly important for MPI to leverage shared memory for intranode communication. This paper investigates the design and optimization of MPI collectives for clusters of NUMA nodes. We develop performance models for collective communication using shared memory and we demonstrate several algorithms for various collectives. Experiments are conducted on both Xeon X5650 and Opteron 6100 InfiniBand clusters. The measurements agree with the model and indicate that different algorithms dominate for short vectors and long vectors. We compare our shared-memory allreduce with several MPI implementations—Open MPI, MPICH2, and MVAPICH2—that utilize system shared memory to facilitate interprocess communication. On a 16-node Xeon cluster and 8-node Opteron cluster, our implementation achieves on geometric average 2.3X and 2.1X speedup over the best MPI implementation, respectively. Our techniques enable an efficient implementation of collective operations on future multi- and manycore systems.     

Evaluation of Soil Contamination by Polycyclic Aromatic Hydrocarbons in Gipuzkoa (Northern Spain)

Representative polycyclic aromatic hydrocarbons (PAHs) of low-medium molecular weight were determined using headspace solid-phase microextraction and gas chromatography with a flame ionization detector (HS-SPME-GC-FID) in ten surface soil samples from Gipuzkoa (Northern Spain). The sum of the PAHs ranged from 0.21 to 136.26 mg kg^?1. Pyrene and chrysene were the most abundant detected PAHs with an average concentration around 3.1 mg kg^?1. Pearson's correlation and PAH isomer ratios were applied to study the different origins of contamination. The results indicated that the PAH contamination in the studied area was a mixed pattern of pyrolytic and petrogenic inputs. Multivariate exploratory techniques, principal component analysis (PCA), and cluster analysis (CA) were also applied corroborating the PAH compounds patterns in the soils.     

A review of methods for analysing spatial and temporal patterns in coastal water quality

Coastal environments contain some of the marine world's most important ecosystems and represent significant resources for human industry and recreation. Water quality in the coastal environment is extremely important for a number of reasons from the protection of marine organisms and the well being of marine ecosystems to the health of people in the region and the safety of industries such as aquaculture. As a result it is essential that environmental health in coastal environments is monitored. Traditional monitoring methods include assessment of biological indices or direct measurements of water quality, which are based on in situ data collection and hence are often spatially or temporally limited. Remote sensing imagery is increasingly used as a rich source of spatial information, providing more detailed coverage then other methods. But the complexity of information in the imagery requires new analysis techniques that allow us to identify the components and possible causes of spatial and temporal variability.This paper presents a review of methods to analyse spatial and temporal variations in remote sensing data of coastal water quality and discusses and compares these methods and the outcomes they achieve. Selected techniques are illustrated by using a sample dataset of MODIS chlorophyll-a imagery. We consider classification methods (cluster analysis, discriminant analysis) that may be used in exploratory, confirmatory and predictive ways, methods that summarize and identify patterns within complex datasets (factor analysis, principal components analysis, self-organizing maps), and techniques that explicitly analyse spatial relationships (the semivariogram and geographically weighted regression). Each technique has a different purpose and addresses different questions. This review identifies how these methods can be utilized to address water quality variability in order to foster a wider application of such techniques for coastal water quality assessment and monitoring.     

Molecular phylogeny of Burkholderia pseudomallei from a remote region of Papua New Guinea

Background

The island of New Guinea is located midway between the world''s two major melioidosis endemic regions of Australia and Southeast Asia. Previous studies in Papua New Guinea have demonstrated autochthonous melioidosis in Balimo, Western province. In contrast to other regions of endemicity, isolates recovered from both environmental and clinical sources demonstrate narrow genetic diversity over large spatial and temporal scales.

Methodology/Principal Findings

We employed molecular typing techniques to determine the phylogenetic relationships of these isolates to each other and to others worldwide to aid in understanding the origins of the Papua New Guinean isolates. Multi-locus sequence typing of the 39 isolates resolved three unique sequence types. Phylogenetic reconstruction and Structure analysis determined that all isolates were genetically closer to those from Australia than those from Southeast Asia. Gene cluster analysis however, identified a Yersinia-like fimbrial gene cluster predominantly found among Burkholderia pseudomallei derived from Southeast Asia. Higher resolution VNTR typing and phylogenetic reconstruction of the Balimo isolates resolved 24 genotypes with long branch lengths. These findings are congruent with long term persistence in the region and a high level of environmental stability.

Conclusions/Significance

Given that anthropogenic influence has been hypothesized as a mechanism for the dispersal of B. pseudomallei, these findings correlate with limited movement of the indigenous people in the region. The palaeogeographical and anthropogenic history of Australasia and the results from this study indicate that New Guinea is an important region for the further study of B. pseudomallei origins and dissemination.     

Sequence analysis of porothramycin biosynthetic gene cluster

The biosynthetic gene cluster of porothramycin, a sequence-selective DNA alkylating compound, was identified in the genome of producing strain Streptomyces albus subsp. albus (ATCC 39897) and sequentially characterized. A 39.7 kb long DNA region contains 27 putative genes, 18 of them revealing high similarity with homologous genes from biosynthetic gene cluster of closely related pyrrolobenzodiazepine (PBD) compound anthramycin. However, considering the structures of both compounds, the number of differences in the gene composition of compared biosynthetic gene clusters was unexpectedly high, indicating participation of alternative enzymes in biosynthesis of both porothramycin precursors, anthranilate, and branched L-proline derivative. Based on the sequence analysis of putative NRPS modules Por20 and Por21, we suppose that in porothramycin biosynthesis, the methylation of anthranilate unit occurs prior to the condensation reaction, while modifications of branched proline derivative, oxidation, and dimethylation of the side chain occur on already condensed PBD core. Corresponding two specific methyltransferase encoding genes por26 and por25 were identified in the porothramycin gene cluster. Surprisingly, also methyltransferase gene por18 homologous to orf19 from anthramycin biosynthesis was detected in porothramycin gene cluster even though the appropriate biosynthetic step is missing, as suggested by ultra high-performance liquid chromatography-diode array detection-mass spectrometry (UHPLC-DAD-MS) analysis of the product in the S. albus culture broth.