A comparison of trace element concentrations in cultured and wild carp (Cyprinus carpio) of Lake Kasumigaura, Japan

The concentrations of 13 elements were determined in the muscle, liver, intestine, kidney, and gonads of cultured and wild carp caught at two sites in Lake Kasumigaura, Japan, between September 1994 and September 1995. Despite having a reputation for being heavily polluted, the carp were not heavily burdened with metals. Our results suggest that despite their dietary differences, the wild and cultured fish were accumulating and distributing metals in the same manner and that aquaculture practices are not increasing metal concentrations in these fish. Metal concentrations were lowest in muscle, and did not exceed established quality standards for fish. The differences in metal concentrations between cultivated and wild carp are negligible and should pose no health problems for consumers of either type of fish.

A new algorithm for the Segre classification of the trace-free Ricci tensor

A new algorithm, based on the introduction of new spinor quantities, for the Segre classification of the trace-free Ricci tensor is presented. It is capable of automatically distinguishing between the two Segre types [1,1(11)] and [(1,1)11] where all other known algorithms fail to do so.

Analyzing security protocols using association rule mining

Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.

Depth first rule heneration for text categorization

Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.

Microarray analysis for gene expression that involves trace metals

Microarray plays a major role to identify the over- and under-expressed genes. It is a well-known fact that trace elements in our body play a major role in the metabolic processes of all living organisms. In this paper, the microarray studies related to major trace metals are reviewed. This review forms the basis for the converged effort to locate the genes that are either defective and destabilise the concentration of the trace metals or influenced by the changed concentration of the trace metals that are needed for proper functions of the human body, at different parts of the body.

Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in Southeastern Australia

In recent times, the apparent population decline of the southern bent-wing bat (Miniopterus schreibersii bassanii) at Bat Cave, Naracoorte has been ascribed to pesticide use in the region, following the finding of organochlorine and orgaonophosphate insecticide residues in bat guano. Adult southern bent-wing bats were collected from Bat Cave and Starlight Cave in 2003. Organochlorine contaminants were detected in all carcass samples: p,p′-DDE was by far the most dominant contaminant with concentrations ranging from 11 000 to 59 000 ng g⁻¹, followed by p,p′-DDT (110–1600 ng g⁻¹), p,p′-DDD (35–620 ng g⁻¹), ∑PCBs (33–490 ng g⁻¹), ∑chlordane and related compounds (7.9–270 ng g⁻¹), HCB (1.6–120 ng g⁻¹), HP epox. (3.1–230 ng g⁻¹), TCPMOH (3.8–38 ng g⁻¹), ∑HCHs (1.4–9.6 ng g⁻¹), and TCPMe (0.1–4.2 ng g⁻¹) (all values on lipid-weight basis). No significant difference in DDE, DDD, DDT, ∑DDT, ∑PCB, trans-chlordane, heptachlor epoxide, trans-nonachlor, α-HCH, β-HCH, γ-HCH, TCPMOH or TCPMe concentrations were observed either between sexes within sites, or between sites (p > 0.05). However, there were significant differences in HCB and oxychlordane concentrations between sexes and between sites (p < 0.05), between site differences in cis-nonachlor concentrations in male bats (p < 0.05), and cis-chlordane concentrations between sexes at Starlight Cave, and between males of each site (p < 0.05). There were also significant differences in the liver concentrations of some metals between sexes within sites (Ag, Cd, Co, Cu, Pb, Se, Zn), and between sites (Ag, Cd, Co, Cu, Hg, Pb, Se, V, Zn). Clustering or grouping of sites was observed when the OC data was expressed on a lipid-weight basis. These inter-site differences in OC concentrations reflect local exposure over a period of time, and do not unambiguously support any suggestion that we are witnessing incipient speciation. However, for conservation purposes, it may be prudent to assume that there are two sub-populations of M. s. bassani feeding in different locations in this region of southern Australia, rather than the single homogeneous population suggested by genetic studies.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

Positivism: one ring to rule them all? A tale of information systems research methodology

The tale of research methodology in information systems is told through the fantasy of Tolkien’s Lord of the Rings. The tale is intended to be at once a piece of light hearted fun in its placement of the struggles of research methodology as an epic story but, in the tradition of the court jester, attempts to provide a new perspective on Information Systems (IS) research methodology and our struggles with positivism in particular. Our tale is one of developing a greater maturity and confidence in IS methodology and introduces postmodern methodologies to Information Systems. Our tale, our pastiche, is itself postmodern.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Measuring moral judgement and the implications of cooperative education and rule-based learning

The Defining Issues Test (DIT), developed by Rest (1986), measures a person's level of moral development using hypothetical social dilemmas. Although the DIT is useful for measuring moral development in social settings, it might not adequately capture an individual's moral judgement abilities in solving work-related problems (Weber, 1990; Trevino, 1992; Welton et al., 1994). In the present study, the moral judgement levels of 97 accounting students were measured over a 1 year period using two separate test instruments, the DIT and a context-specific instrument developed by Welton et al. (1994). The test scores are significantly higher on the DIT than the Welton instrument (between the instruments and over time), suggesting that accounting students use higher levels of moral reasoning in resolving hypothetical social dilemmas and lower levels of moral reasoning in resolving context-specific dilemmas. The difference in test scores was highest during cooperative education (work placement programme), implying that the environment is a significant determinant on students' test scores.