Development of a short-term model using petrophysical logging at Century Mine, North Queensland

Pasminco Century Mine has developed a geophysical logging system to provide new data for ore mining/grade control and the generation of Short Term Models for mine planning. Previous work indicated the applicability of petrophysical logging for lithology prediction, however, the automation of the method was not considered reliable enough for the development of a mining model. A test survey was undertaken using two diamond drilled control holes and eight percussion holes. All holes were logged with natural gamma, magnetic susceptibility and density. Calibration of the LogTrans auto-interpretation software using only natural gamma and magnetic susceptibility indicated that both lithology and stratigraphy could be predicted. Development of a capability to enforce stratigraphic order within LogTrans increased the reliability and accuracy of interpretations. After the completion of a feasibility program, Century Mine has invested in a dedicated logging vehicle to log blast holes as well as for use in in-fill drilling programs. Future refinement of the system may lead to the development of GPS controlled excavators for mining ore.

Spontaneous-combustion propensity of New Zealand coals under adiabatic conditions

Pulverised New Zealand coal samples have been tested from an initial temperature of 40 degreesC and reacted adiabatisally in an oven with oxygen to provide a full temperature history of auto-oxidation up to the self-sustained process of combustion. This procedure produces a self-heating rate index, R-70, calculated as the ratio of the time taken to reach 70 degreesC (degreesC/h). The R-70 index is a measure of the coal's propensity to spontaneous combustion. R-70 values for New Zealand coals are much higher than any previously published results. They show a rank dependence, whereby subbituminous coals have the highest propensity to spontaneous combustion (14.91-17.23 degreesC/h). A lignite sample has an R-70 value of 7.76 degreesC/h, and high-volatile bituminous B coals have R-70 values of 0.31-2.23 degreesC/h. Samples stored for 2 years show the same rank trend. The nature of this trend is most likely a function of the internal surface area of the coal that governs the available sites for oxidation. Calculating the Suggate rank; for any New Zealand coal can be used to rare its propensity to spontaneous combustion. Resin bodies in the subbituminous coal show no propensity to spontaneous combustion. (C) 2001 Elsevier Science B.V. All rights reserved.

Mining wheat germplasm collections for yield enhancing traits

The material in genebanks includes valuable traditional varieties and landraces, non-domesticated species, advanced and obsolete cultivars, breeding lines and genetic stock. It is the wide variety of potentially useful genetic diversity that makes collections valuable. While most of the yield increases to date have resulted from manipulation of a few major traits (such as height, photoperiodism, and vernalization), meeting future demand for increased yields will require exploitation of novel genetic resources. Many traits have been reported to have potential to enhance yield, and high expression of these can be found in germplasm collections. To boost yield in irrigated situations, spike fertility must be improved simultaneously with photosynthetic capacity. CIMMYT's Wheat Genetic Resources program has identified a source of multi-ovary florets, with up to 6 kernels per floret. Lines from landrace collections have been identified that have very high chlorophyll concentration, which may increase leaf photosynthetic rate. High chlorophyll concentration and high stomatal conductance are associated with heat tolerance. Recent studies, through augmented use of seed multiplication nurseries, identified high expression of these traits in bank accessions, and both traits were heritable. Searches are underway for drought tolerance traits related to remobilization of stem fructans, awn photosynthesis, osmotic adjustment, and pubescence. Genetic diversity from wild relatives through the production of synthetic wheats has produced novel genetic diversity.

The design of hazard risk assessment matrices for ranking occupational health risks and their application in mining and minerals processing

Two hazard risk assessment matrices for the ranking of occupational health risks are described. The qualitative matrix uses qualitative measures of probability and consequence to determine risk assessment codes for hazard-disease combinations. A walk-through survey of an underground metalliferous mine and concentrator is used to demonstrate how the qualitative matrix can be applied to determine priorities for the control of occupational health hazards. The semi-quantitative matrix uses attributable risk as a quantitative measure of probability and uses qualitative measures of consequence. A practical application of this matrix is the determination of occupational health priorities using existing epidemiological studies. Calculated attributable risks from epidemiological studies of hazard-disease combinations in mining and minerals processing are used as examples. These historic response data do not reflect the risks associated with current exposures. A method using current exposure data, known exposure-response relationships and the semi-quantitative matrix is proposed for more accurate and current risk rankings.

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.

New arsenite-oxidizing bacteria isolated from Australian gold mining environments - Phylogenetic relationships

Nine novel arsenite-oxidizing bacteria have been isolated from two different gold mine environments in Australia. Four of these organisms grow chemolithoautotrophically with oxygen as the terminal electron acceptor, arsenite as the electron donor, and carbon dioxide-bicarbonate as the sole carbon source. Five heterotrophic arsenite-oxidizing bacteria were also isolated, one of which was found to be both phylogenetically and physiologically identical to the previously described heterotrophic arsenite oxidizer misidentified as Alcaligenes faecalis. The results showed that this strain belongs to the genus Achromobacter. Phylogenetically, the arsenite-oxidizing bacteria fall within two separate subdivisions of the Proteobacteria. Interestingly, the chemolithoautotrophic arsenite oxidizers belong to the alpha-Proteobacteria, whereas the heterotrophic arsenite oxidizers belong to the beta-Proteobacteria.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.