A multiple-outgroup approach to resolving division-level phylogenetic relationships using 16S rDNA data

The 16S rRNA gene (16S rDNA) is currently the most widely used gene for estimating the evolutionary history of prokaryotes, To date, there are more than 30 000 16S rDNA sequences available from the core databases, GenBank, EMBL and DDBJ, This great number may cause a dilemma when composing datasets for phylogenetic analysis, since the choice and number of reference organisms are known to affect the resulting tree topology. A group of sequences appearing monophyletic in one dataset may not be so in another. This can be especially problematic when establishing the relationships of distantly related sequences at the division (phylum) level. In this study, a multiple-outgroup approach to resolving division-level phylogenetic relationships is suggested using 16S rDNA data. The approach is illustrated by two case studies concerning the monophyly of two recently proposed bacterial divisions, OP9 and OP10.

A new approach for detecting and mapping sewage impacts

Increased nitrogen loading has been implicated in eutrophication occurrences worldwide. Much of this loading is attributable to the growing human population along the world's coastlines. A significant component of this nitrogen input is from sewage effluent, and delineation of the distribution and biological impact of sewage-derived nitrogen is becoming increasingly important. Here, we show a technique that identifies the source, extent and fate of biologically available sewage nitrogen in coastal marine ecosystem. This method is based on the uptake of sewage nitrogen by marine plants and subsequent analysis of the sewage signature (elevated delta N-15) in plant tissues. Spatial analysis is used to create maps of delta N-15 and establish coefficient of variation estimates of the mapped values. We show elevated delta N-15 levels in marine plants near sewage outfalls in Moreton Bay, Australia, a semi-enclosed bay receiving multiple sewage inputs. These maps of sewage nitrogen distribution are being used to direct nutrient reduction strategies in the region and will assist in monitoring the effectiveness of environmental protection measures. (C) 2001 Elsevier Science Ltd. All rights reserved.

A comparison of the lateral and posterior retroperitoneoscopic approach for complete and partial nephroureterectomy in children

Objective To report the comparative results of a selective posterior or lateral retroperitoneoscopic approach (RPA) for nephroureterectomy in children. Patients and methods Following an established experience with RPA, 36 complete and 19 partial nephrouretectomies were prospectively randomized to a posterior and lateral retroperitoneoscopic approach. The patients were aged 4 months to 14 years, with a body weight at operation of 5.7-82 kg. For posterior RPA the child is positioned prone, with three access ports. The operating space was created with balloon dissection and maintained with CO2 insufflation. The child was then rotated 30 degrees with the kidney in the dependent position, and the operator and assistant standing on the affected side. In the lateral approach the child is in the lateral decubitus position with the operator and assistant facing the dorsal aspect of the patient. Results There was no significant difference in operative duration between the lateral and posterior approaches for nephrectomy (65 and 47 min) or partial nephrectomy (85 and 75 min). Two lateral nephrectomies required open conversion (one upper pole and one lower pole). Conclusion The posterior approach gives easy and quick access to the renal pedicle. It is preferable for complete nephrectomy alone and partial or polar excision. In children under 5 years old a near complete ureterectomy can be achieved. The lateral approach creates more inferomedial space, gives better access to ectopic kidneys and allows complete ureterectomy in all cases, Access to the pedicle in the normal position requires more frequent positioning of the kidney. Care must be taken as peritoneal tears are more common.

Compact clinical MRI magnet design using a multilayer current density approach

In this work, a new method of optimization is successfully applied to the theoretical design of compact, actively shielded, clinical MRI magnets. The problem is formulated as a two-step process in which the desired current densities on multiple, cc-axial surface layers are first calculated by solving Fredholm equations of the first kind. Non-linear optimization methods with inequality constraints are then invoked to fit practical magnet coils to the desired current densities. The current density approach allows rapid prototyping of unusual magnet designs. The emphasis of this work is on the optimal design of short, actively-shielded MRI magnets for whole-body imaging. Details of the hybrid numerical model are presented, and the model is used to investigate compact, symmetric, and asymmetric MRI magnets. Magnet designs are presented for actively-shielded, symmetric magnets of coil length 1.0 m, which is considerably shorter than currently available designs of comparable dsv size. Novel, actively-shielded, asymmetric magnet designs are also presented in which the beginning of a 50-cm dsv is positioned just 11 cm from the end of the coil structure, allowing much improved access to the patient and reduced patient claustrophobia. Magn Reson Med 45:331540, 2001. (C) 2001 Wiley-Liss, Inc.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Resolving the puzzle of productive and destructive conflict in culturally heterogeneous workgroups: A communication accommodation theory approach

This study presents an investigation of the communicative behaviors and strategies employed in the stimulation and management of productive and destructive conflict in culturally heterogeneous workgroups. Using communication accommodation theory (CAT), we argue that the type and course of conflict in culturally heterogeneous workgroups is impacted by the communicative behaviors and strategies employed by group members during interactions. Analysis of data from participant observations, non-participant observations, semi-structured interviews, and self-report questionnaires support CA T-based predictions and provide fresh insights into the triggers and management strategies associated with conflict in culturally heterogeneous workgroups. In particular, results indicated that the more groups used discourse management Strategies, the more they experienced productive conflict. In addition, the use of explanation and checking of own and others' understanding was a major feature of productive conflict, while speech interruptions emerged as a strategy leading to potential destructive conflict. Groups where leaders emerged and assisted in reversing communication breakdowns were better able to manage their discourse, and achieved consensus On task processes. Contributions to the understanding of the triggers and the management of productive conflict in culturally heterogeneous workgroups are discussed.