Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Knowledge, economy, technology and society: The politics of discourse

The WSIS is centrally interested in knowledge and has defined for itself a mission that is broadly humanitarian. Its development ‘talk’ is, rightly, replete with notions of equity, preserving culture, justice, human rights and so on. In incorporating such issues into knowledge society and economy discussions, WSIS has adopted a different posture towards knowledge than is seen in dominant discourses. This study analyses the dominant knowledge discourse using a large corpus of knowledge-related policy documents, discourse theory and an interrelational understanding of knowledge. I show that it is important to understand this dominant knowledge discourse because of its capacity to limit thought and action in relation to its central topic, knowledge. The results of this study demonstrate that the dominant knowledge discourse is technocratic, frequently insensitive to the humane mission at the core of the WSIS, and is based on a partial understanding of what knowledge is and how knowledge systems work. Moreover, I show that knowledge is inherently political, that the dominant knowledge discourse is politically oriented towards the concerns of business and technology, but that an emancipatory politics of knowledge is possible.

Debiasing the outcome effect: The role of instructions in an audit litigation setting

The outcome effect occurs where an evaluator, who has knowledge of the outcome of a judge's decision , assesses the quality of the judgment of that decision maker. If the evaluator has knowledge of a negative outcome, then that knowledge negatively influences his or her assessment of the ex ante judgment. For instance, jurors in a lawsuit brought against an auditor for alleged negligence are informed of an undetected fraud, even though an unqualified opinion was issued. This paper reports the results of an experiment in an applied audit judgment setting that examined methods of mitigating the outcome effect by means of instructions. The results showed that simply instructing or warning the evaluator about the potential biasing effects of outcome information was only weakly effective. However, instructions that stressed either (1) the cognitive non-normativeness of the outcome effect or (2) the seriousness and gravity of the evaluation ameliorated the effect significantly. From a theoretical perspective, the results suggest that there may both motivational and cognitive components to the outcome effect. In all, the findings suggest awareness of the outcome effect and use of relatively nonintrusive instructions to evaluators may effectively counteract the potential for the outcome bias.

Recent advances in our knowledge of the Myxozoa

In the last few years two factors have helped to significantly advance our understanding of the Myxozoa. First, the phenomenal increase in fin fish aquaculture in the 1990s has lead to the increased importance of these parasites; in rum this has lead to intensified research efforts, which have increased knowledge of the development, diagnosis, and pathogenesis of myxozoans. The hallmark discovery in the 1980s that the life cycle of Myxobolus cerebralis requires development of an actinosporean stage in the Oligochaete. Tubifex tubifex, led to the elucidation of the life cycles of several other myxozoans. Also, the life cycle and taxonomy of the enigmatic PKX myxozoan has been resolved: it is the alternate stage of the unusual myxozoan. Tetracapsula bryosalmonae, from bryozoans. The 18S rDNA gene of many species has been sequenced, and here we add 22 new sequences to the data set. Phylogenetic analyses using all these sequences indicate that: 1) the Myxozoa are closely related to Cnidaria (also supported by morphological data), 2) marine taxa at the genus level branch separately from genera that usually infect freshwater fishes; 3) taxa cluster more by development and tissue location than by spore morphology; 4) the tetracapsulids branched off early in myxozoan evolution, perhaps reflected by their having bryozoan. rather than annelid hosts; 5) the morphology of actinosporeans offers little information for determining their myxosporean counterparts (assuming that they exist), and 6) the marine actinosporeans from Australia appear to form a clade within the platysporinid myxosporeans. Ribosomal DNA sequences have also enabled development of diagnostic tests for myxozoans. PCR and in situ hybridisation tests based on rDNA sequences have been developed for Myxobolus cerebralis. Ceratomyxa shasta. Kudoa spp,, and Tetracapsula bryosalmonae (PKX). Lectin-based and antibody tests have also been developed for certain myxozoans, such as PKX and C. shasta. We also review important diseases caused by myxozoans. which are emerging or re-emerging. Epizootics of whirling disease in wild rainbow trout (Oncorhynchus mykiss) have recently been reported throughout the Rocky Mountain states of the USA. With a dramatic increase in aquaculture of fishes using marine netpens, several marine myxozoans have been recognized or elevated in status as pathological agents. Kudoa thyrsites infections have caused severe post-harvest myoliquefaction in pen-reared Atlantic salmon (Salmo salar), and Ceratomyxa spp., Sphaerospora spp., and Myxidium leei cause disease in pen-reared sea bass (Dicentrarchus labrax) and sea bream species (family Sparidae) in Mediterranean countries.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

MHCWeb: Converting a WWW database into a knowledge-based collaborative environment

The World Wide Web (WWW) is useful for distributing scientific data. Most existing web data resources organize their information either in structured flat files or relational databases with basic retrieval capabilities. For databases with one or a few simple relations, these approaches are successful, but they can be cumbersome when there is a data model involving multiple relations between complex data. We believe that knowledge-based resources offer a solution in these cases. Knowledge bases have explicit declarations of the concepts in the domain, along with the relations between them. They are usually organized hierarchically, and provide a global data model with a controlled vocabulary, We have created the OWEB architecture for building online scientific data resources using knowledge bases. OWEB provides a shell for structuring data, providing secure and shared access, and creating computational modules for processing and displaying data. In this paper, we describe the translation of the online immunological database MHCPEP into an OWEB system called MHCWeb. This effort involved building a conceptual model for the data, creating a controlled terminology for the legal values for different types of data, and then translating the original data into the new structure. The 0 WEB environment allows for flexible access to the data by both users and computer programs.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Crocodile farms and management of saltwater crocodiles in Northern Territory: Results of a survey of NT crocodile farmers plus analysis of secondary information

After outlining some relevant background information about the NT crocodile farming industry and explaining the purpose of our survey of NT crocodile farmers conducted in the first half of 2005, this paper reports the results of the survey. The information received from the survey is supplemented by secondary data and by information from secondary sources. This report covers the location of respondents; the size of crocodile farms; farmers’ stated knowledge of and attitudes towards the NT Crocodile Management Plan; the involvement of farms in the harvesting of crocodile eggs and the costs involved; views of crocodile farmers about whether the NT Crocodile Management Plan encourages landholders to conserve crocodiles and their perceptions of the benefits to landholders; predicted production trends and trends in the number of farms operating in NT; economic characteristics of crocodile farms producing in NT including the economic advantages and disadvantages of crocodile farming in NT. Concluding comments provide, amongst other things, an overview of the structure of the crocodile farming industry in the NT gleaned from a consideration of data available from the NT Government’s Department of Business, Industry and Resource Development.

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.