Fitting a mixture model to three-mode three-way data with missing information

When the data consist of certain attributes measured on the same set of items in different situations, they would be described as a three-mode three-way array. A mixture likelihood approach can be implemented to cluster the items (i.e., one of the modes) on the basis of both of the other modes simultaneously (i.e,, the attributes measured in different situations). In this paper, it is shown that this approach can be extended to handle three-mode three-way arrays where some of the data values are missing at random in the sense of Little and Rubin (1987). The methodology is illustrated by clustering the genotypes in a three-way soybean data set where various attributes were measured on genotypes grown in several environments.

Further studies on an intermediate host murine model showing that a primary Echinococcus granulosus infection is protective against subsequent oncospheral challenge

We describe the use of a murine model to evaluate resistance against subsequent challenge following a primary infection with oncospheres of Echinococcus granulosus. Mice (Kunming strain) were infected with hatched oncospheres of Echinococcus granulosus; 21 days later a second challenge was given by a different route of infection. A primary infection by intraperitoneal (i.p.) injection stimulated 100 and 90.5% protection in terms of reduced cyst numbers against a secondary infection given subcutaneously (s.c.) or intravenously (i.v.) respectively. A primary infection given s.c. followed by i.p. or i.v. challenge resulted in 84.0 and 100% protection, respectively. Intravenous infection followed by i.p. or s.c. challenge resulted in 98.5 and 69.4% protection, respectively. With the i.v. route of infection, almost all resultant cysts were present in the lungs. The data show that a primary infection with oncospheres can induce total or a high degree of protection against a subsequent challenge and confirms that natural (concomitant) immunity can be stimulated in the intermediate host as the result of a primary infection. This may explain the decline in hydatid infection in sheep older than 2 years in hyper-endemic areas such as those found in Xingjiang, China. These older sheep may have been earlier infected and have subsequently self-cured, with the primary infection stimulating an immune response that protects the intermediate host animals from further infection. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.

Exploratory spatial data analysis techniques for examining urban crime

Regional planners, policy makers and policing agencies all recognize the importance of better understanding the dynamics of crime. Theoretical and application-oriented approaches which provide insights into why and where crimes take place are much sought after. Geographic information systems and spatial analysis techniques, in particular, are proving to be essential or studying criminal activity. However, the capabilities of these quantitative methods continue to evolve. This paper explores the use of geographic information systems and spatial analysis approaches for examining crime occurrence in Brisbane, Australia. The analysis highlights novel capabilities for the analysis of crime in urban regions.

Accumulation of manganese in Neisseria gonorrhoeae correlates with resistance to oxidative killing by superoxide anion and is independent of superoxide dismutase activity

As a facultative aerobe with a high iron requirement and a highly active aerobic respiratory chain, Neisseria gonorrhoeae requires defence systems to respond to toxic oxygen species such as superoxide. It has been shown that supplementation of media with 100 muM Mn(II) considerably enhanced the resistance of this bacterium to oxidative killing by superoxide. This protection was not associated with the superoxide dismutase enzymes of N. gonorrhoeae. In contrast to previous studies, which suggested that some strains of N. gonorrhoeae might not contain a superoxide dismutase, we identified a sodB gene by genome analysis and confirmed its presence in all strains examined by Southern blotting, but found no evidence for sodA or sodC. A sodB mutant showed very similar susceptibility to superoxide killing to that of wild-type cells, indicating that the Fe-dependent SOD B did not have a major role in resistance to oxidative killing under the conditions tested. The absence of a sodA gene indicated that the Mn-dependent protection against oxidative killing was independent of Mn-dependent SOD A. As a sodB mutant also showed Mn-dependent resistance to oxidative killing, then it is concluded that this resistance is independent of superoxide dismutase enzymes. Resistance to oxidative killing was correlated with accumulation of Mn(II) by the bacterium. We hypothesize that this bacterium uses Mn(II) as a chemical quenching agent in a similar way to the already established process in Lactobacillus plantarum. A search for putative Mn(II) uptake systems identified an ABC cassette-type system (MntABC) with a periplasmic-binding protein (MntC). An mntC mutant was shown to have lowered accumulation of Mn(II) and was also highly susceptible to oxidative killing, even in the presence of added Mn(II). Taken together, these data show that N. gonorrhoeae possesses a Mn(II) uptake system that is critical for resistance to oxidative stress.

Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

Exploring through cover - the integrated interpretation of high resolution aeromagnetic, airborne electromagnetic and ground gravity data from the Grant's Patch area, Eastern Goldfields Province, Archaean Yigarn Craton Part B; Gravity inversion as a

The cost and risk associated with mineral exploration in Australia increases significantly as companies move into deeper regolith-covered terrain. The ability to map the bedrock and the depth of weathering within an area has the potential to decrease this risk and increase the effectiveness of exploration programs. This paper is the second in a trilogy concerning the Grant's Patch area of the Eastern Goldfields. The recent development of the VPmg potential field inversion program in conjunction with the acquisition of high-resolution gravity data over an area with extensive drilling provided an opportunity to evaluate three-dimensional gravity inversion as a bedrock and regolith mapping tool. An apparent density model of the study area was constructed, with the ground represented as adjoining 200 m by 200 m vertical rectangular prisms. During inversion VPmg incrementally adjusted the density of each prism until the free-air gravity response of the model replicated the observed data. For the Grant's Patch study area, this image of the apparent density values proved easier to interpret than the Bouguer gravity image. A regolith layer was introduced into the model and realistic fresh-rock densities assigned to each basement prism according to its interpreted lithology. With the basement and regolith densities fixed, the VPmg inversion algorithm adjusted the depth to fresh basement until the misfit between the calculated and observed gravity response was minimised. The resulting geometry of the bedrock/regolith contact largely replicated the base of weathering indicated by drilling with predicted depth of weathering values from gravity inversion typically within 15% of those logged during RAB and RC drilling.

Investigating technical and scale efficiencies of Australian universities through data envelopment analysis

Performance indicators in the public sector have often been criticised for being inadequate and not conducive to analysing efficiency. The main objective of this study is to use data envelopment analysis (DEA) to examine the relative efficiency of Australian universities. Three performance models are developed, namely, overall performance, performance on delivery of educational services, and performance on fee-paying enrolments. The findings based on 1995 data show that the university sector was performing well on technical and scale efficiency but there was room for improving performance on fee-paying enrolments. There were also small slacks in input utilisation. More universities were operating at decreasing returns to scale, indicating a potential to downsize. DEA helps in identifying the reference sets for inefficient institutions and objectively determines productivity improvements. As such, it can be a valuable benchmarking tool for educational administrators and assist in more efficient allocation of scarce resources. In the absence of market mechanisms to price educational outputs, which renders traditional production or cost functions inappropriate, universities are particularly obliged to seek alternative efficiency analysis methods such as DEA.

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.