A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

Limitations of the manual supination resistance test

The aim of this study was to asses results obtained from a range of commonly performed lower extremity “open and closed” chain kinetic tests used for predicting foot function and correlate these test findings to data obtained from the Zebris WinFDM-T system®. When performed correctly these tests are thought to be indicators of lower extremity function. Podiatrists frequently perform examinations of joint and muscle structures to understand biomechanical function; however the relationship between these routine tests and forces generated during the gait cycle are not always well understood. This can introduce a degree of variability in clinical interpretation which creates conjecture regarding the value of these tests.

Next Generation SHRM Research: From Covariation to Causation

[Excerpt] In response to the longstanding and repeated criticisms that HR does not add value to organizations, the past 10 years has seen a burgeoning of research attempting to demonstrate that progressive HR practices result in higher organizational performance. Huselid’s (1995)groundbreaking study demonstrated that a set of HR practices he referred to as High Performance Work Systems (HPWS) were related to accounting profits and market value of firms. Since then, a number of studies have shown similar positive relationships between HR practices and various measures of firm performance. While the studies comprising what I refer to as “first generation SHRM research” have added to what is becoming a more convincing body of evidence of the positive relationship between HR and performance, this body tends to lack sufficient data to demonstrate that the relationship is actually causal in the sense that HR practices, when instituted, lead to higher performance. This next generation of SHRM research will begin (and, in fact has begun) to focus on designing more rigorous tests of the hypothesis that employing progressive HRM systems actually results in higher organizational performance. This generation of research will focus on two aspects: demonstrating the HRM value chain, and proving causality as opposed to merely covariation.

Supervisors' emerging role in second-generation knowledge management

This study examines supervisors' emerging new role in a technical customer service and home customers division of a large Finnish telecommunications corporation. Data of the study comes from a second-generation knowledge management project, an intervention research, which was conducted for supervisors of the division. The study exemplifies how supervision work is transforming in high technology organization characterized with high speed of change in technologies, products, and in grass root work practices. The intervention research was conducted in the division during spring 2000. Primary analyzed data consists of six two-hour videorecorded intervention sessions. Unit of analysis has been collective learningactions. Researcher has first written conversation transcripts out of the video-recorded meetings and then analyzed this qualitative data using analytical schema based on collective learning actions. Supervisors' role is conceptualized as an actor of a collective and dynamic activity system, based on the ideas from cultural historical activity theory. On knowledge management researcher has takena second-generation knowledge management viewpoint, following ideas fromcultural historical activity theory and developmental work research. Second-generation knowledge management considers knowledge embedded and constructed in collective practices, such as innovation networks or communities of practice (supervisors' work community), which have the capacity to create new knowledge. Analysis and illustration of supervisors' emerging new role is conceptualized in this framework using methodological ideas derived from activity theory and developmental work research. Major findings of the study show that supervisors' emerging new role in a high technology telecommunication organization characterized with high speed of discontinuous change in technologies, products, and in grass-root practices cannot be defined or characterized using a normative management role/model. Their role is expanding two-dimensionally, (1) socially and (2) in new knowledge, and work practices. The expansion in organization and inter-organizational network (social expansion) causes pressures to manage a network of co-operation partners and subordinates. On the other hand, the faster speed of change in technological solutions, new products, and novel customer wants (expansion in knowledge) causes pressures for supervisors to innovate quickly new work practices to manage this change. Keywords: Activity theory, knowledge management, developmental work research, supervisors, high technology organizations, telecommunication organizations, second-generation knowledge management, competence laboratory, intervention research, learning actions.

Comprehensive Path-sensitive Data-flow Analysis

Data-flow analysis is an integral part of any aggressive optimizing compiler. We propose a framework for improving the precision of data-flow analysis in the presence of complex control-flow. W initially perform data-flow analysis to determine those control-flow merges which cause the loss in data-flow analysis precision. The control-flow graph of the program is then restructured such that performing data-flow analysis on the resulting restructured graph gives more precise results. The proposed framework is both simple, involving the familiar notion of product automata, and also general, since it is applicable to any forward data-flow analysis. Apart from proving that our restructuring process is correct, we also show that restructuring is effective in that it necessarily leads to more optimization opportunities. Furthermore, the framework handles the trade-off between the increase in data-flow precision and the code size increase inherent in the restructuring. We show that determining an optimal restructuring is NP-hard, and propose and evaluate a greedy strategy. The framework has been implemented in the Scale research compiler, and instantiated for the specific problem of Constant Propagation. On the SPECINT 2000 benchmark suite we observe an average speedup of 4% in the running times over Wegman-Zadeck conditional constant propagation algorithm and 2% over a purely path profile guided approach.

Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Physio-chemical assessment of beauty leaf (Calophyllum inophyllum) as second-generation biodiesel feedstock

Recently, second-generation (non-vegetable oil) feedstocks for biodiesel production are receiving significant attention due to the cost and social effects connected with utilising food products for the production of energy products. The Beauty leaf tree (Calophyllum inophyllum) is a potential source of non-edible oil for producing second-generation biodiesel because of its suitability for production in an extensive variety of atmospheric condition, easy cultivation, high fruit production rate, and the high oil content in the seed. In this study, oil was extracted from Beauty leaf tree seeds through three different oil extraction methods. The important physical and chemical properties of these extracted Beauty leaf oils were experimentally analysed and compared with other commercially available vegetable oils. Biodiesel was produced using a two-stage esterification process combining of an acid catalysed pre-esterification process and an alkali catalysed transesterification process. Fatty acid methyl ester (FAME) profiles and important physicochemical properties were experimentally measured and estimated using equations based on the FAME analysis. The quality of Beauty leaf biodiesels was assessed and compared with commercially available biodiesels through multivariate data analysis using PROMETHEE-GAIA software. The results show that mechanical extraction using a screw press produces oil at a low cost, however, results in low oil yields compared with chemical oil extraction. High pressure and temperature in the extraction process increase oil extraction performance. On the contrary, this process increases the free fatty acid content in the oil. A clear difference was found in the physical properties of Beauty leaf oils, which eventually affected the oil to biodiesel conversion process. However, Beauty leaf oils methyl esters (biodiesel) were very consistent physicochemical properties and able to meet almost all indicators of biodiesel standards. Overall this study found that Beauty leaf is a suitable feedstock for producing second-generation biodiesel in commercial scale. Therefore, the findings of this study are expected to serve as the basis for further development of Beauty leaf as a feedstock for industrial scale second-generation biodiesel production.

Management Guidelines for New Warm-Season Grasses in Australia

After more than 30 years in which ‘Tifgreen’ and ‘Tifdwarf’ were the only greens-quality varieties available, the choice for golf courses and bowls clubs in northern Australia has been expanded to include six new Cynodon hybrids [Cynodon dactylon (L.) Pers x Cynodon transvaalensis Burtt-Davy]. Five of these – ‘Champion Dwarf’ (Texas), ‘MS-Supreme’ (Mississippi), FloraDwarf™ (Florida), ‘TifEagle’ (Georgia), MiniVerde™ (Arizona) - are from US breeding programs, while the sixth, ‘TL2’ (marketed as Novotek™) was selected in north Queensland. The finer, denser and lower growing habit of the “ultradwarf” cultivars allows very low mowing heights (e.g. 2.5 mm) to be imposed, resulting in denser and smoother putting and bowls surfaces. In addition to the Cynodon hybrids, four new greens quality seashore paspalum (Paspalum vaginatum O. Swartz) cultivars including ‘Sea Isle 2000’, Sea Isle Supreme™, Velvetene™ and Sea Dwarf™ (where tolerance of salty water is required) expands the range of choices for greens in difficult environments. The project was developed to determine (a) the appropriate choice of cultivar for different environments and budgets, and (b) best management practices for the new cultivars which differ from the Cynodon hybrid industry standards ‘Tifgreen’ and ‘Tifdwarf’. Management practices, particularly fertilising, mowing heights and frequency, and thatch control were investigated to determine optimum management inputs and provide high quality playing surfaces with the new grasses. To enable effective trialling of these new and old cultivars it was essential to have a number of regional sites participating in the study. Drought and financial hardship of many clubs presented an initial setback with numerous clubs wanting to be involved in the study but were unable to commit due to their financial position at the time. The study was fortunate to have seven regional sites from Queensland, New South Wales, Victoria and South Australia volunteer to be involved in the study which would add to the results being collected at the centralised test facility being constructed at DEEDI’s Redlands Research Station. The major research findings acquired from the eight trial sites included: • All of the new second generation “ultradwarf” couchgrasses tend to produce a large amount of thatch with MiniVerde™ being the greatest thatch producer, particularly compared to ‘Tifdwarf’ and ‘Tifgreen’. The maintenance of the new Cynodon hybrids will require a program of regular dethatching/grooming as well as regular light dustings of sand. Thatch prevention should begin 3 to 4 weeks after planting a new “ultradwarf” couchgrass green, with an emphasis on prevention rather than control. • The “ultradwarfs” produced faster green speeds than the current industry standards ‘Tifgreen’ and ‘Tifdwarf’. However, all Cynodon hybrids were considerably faster than the seashore paspalums (e.g. comparable to the speed diference of Bentgrass and couchgrass) under trial conditions. Green speed was fastest being cut at 3.5 mm and rolled (compared to 3.5 mm cut, no roll and 2.7 mm cut, no roll). • All trial sites reported the occurrence of disease in the Cynodon hybrids with the main incidence of disease occurring during the dormancy period (autumn and winter). The main disease issue reported was “patch diseases” which includes both Gaumannomyces and Rhizoctonia species. There was differences in the severity of the disease between cultivars, however, the severity of the disease was not consistent between cultivars and is largely attributed to an environment (location) effect. In terms of managing the occurrence of disease, the incidence of disease is less severe where there is a higher fertility rate (about 3 kgN/100m2/year) or a preventitatve fungicide program is adopted. • Cynodon hybrid and seashore paspalum cultivars maintained an acceptable to ideal surface being cut between 2.7 mm and 5.0 mm. “Ultradwarf” cultivars can tolerate mowing heights as low as 2.5 mm for short periods but places the plant under high levels of stress. Greens being maintained at a continually lower cutting height (e.g. 2.7 mm) of both species is achievable, but would need to be cut daily for best results. Seashore paspalums performed best when cut at a height of between 2.7 mm and 3.0 mm. If a lower cutting height is adopted, regular and repeated mowings are required to reduce scalping and produce a smooth surface. • At this point in time the optimum rate of nitrogen (N) for the Cynodon hybrids is 3 kg/100m2/year and while the seashore paspalums is 2 to 3 kg/100m2/year. • Dormancy occurred for all Cynodon and seashore paspalum culitvars from north in Brisbane (QLD) to south in Mornington Peninsula (VIC) and west to Novar Gardens (SA). Cynodon and Paspalum growth in both Victoria and South Australia was less favourable as a result of the cooler climates. • After combining the data collected from all eight sites, the results indicated that there can be variation (e.g. turfgrass quality, colour, disease resistance, performace) depending on the site and climatic conditions. Such evidence highlights the need to undertake genotype by environment (G x E) studies on new and old cultivars prior to conversion or establishment. • For a club looking to select either a Cynodon hybrid or seashore paspalum cultivar for use at their club they need to: - Review the research data. - Look at trial plots. - Inspect greens in play that have the new grasses. - Select 2 to 3 cultivars that are considered to be the better types. - Establish them in large (large enough to putt on) plots/nursery/practice putter. Ideally the area should be subjected to wear. - Maintain them exactly as they would be on the golf course/lawn bowls green. This is a critical aspect. Regular mowing, fertilising etc. is essential. - Assess them over at least 2 to 3 years. - Make a selection and establish it in a playing green so that it is subjected to typical wear.

Using genomic data to make indirect (and unauthorized) estimates of disease risk

The number of genetic factors associated with common human traits and disease is increasing rapidly, and the general public is utilizing affordable, direct-to-consumer genetic tests. The results of these tests are often in the public domain. A combination of factors has increased the potential for the indirect estimation of an individual's risk for a particular trait. Here we explain the basic principals underlying risk estimation which allowed us to test the ability to make an indirect risk estimation from genetic data by imputing Dr. James Watson's redacted apolipoprotein E gene (APOE) information. The principles underlying risk prediction from genetic data have been well known and applied for many decades, however, the recent increase in genomic knowledge, and advances in mathematical and statistical techniques and computational power, make it relatively easy to make an accurate but indirect estimation of risk. There is a current hazard for indirect risk estimation that is relevant not only to the subject but also to individuals related to the subject; this risk will likely increase as more detailed genomic data and better computational tools become available.

A versatile gene-based test for genome-wide association studies

We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in approximately 1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.

A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was approximately 80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations.

The development and preliminary testing of an instrument for assessing fatigue self-management outcomes in patients with advanced cancer

Background: Fatigue is one of the most distressing and commonly experienced symptoms in patients with advanced cancer. Although the self-management (SM) of cancer-related symptoms has received increasing attention, no research instrument assessing fatigue SM outcomes for patients with advanced cancer is available. Objectives: to describe the development and preliminary testing of an interviewer administered instrument for assessing the frequency, and perceived levels of effectiveness and self-efficacy associated with fatigue SM behaviors in patients with advanced cancer. Methods: The development and testing of the Self-efficacy in Managing Symptoms Scale- Fatigue Subscale for Patients with Advanced Cancer (SMSFS-A) involved a number of procedures: item-generation using a comprehensive literature review and semi-structured interviews, content validity evaluation using expert panel reviews, and face validity and test-retest reliability evaluation using pilot testing. Results: Initially, 23 items (22 specific behaviors with one global item) were generated from the literature review and semi-structured interviews. After two rounds of expert panel review, the final scale was reduced to 17 items (16 behaviors with one global item). Participants in the pilot test (n=10) confirmed that the questions in this scale were clear and easy to understand. Bland-Altman analysis showed agreement of results over a one-week interval. Conclusions: The SMSFS-A items were generated using multiple sources. This tool demonstrated preliminary validity and reliability. Implications for practice: The SMSFS-A has the potential to be used for clinical and research purposes. Nurses can use this instrument for collecting data to inform the initiation of appropriate fatigue SM support for this population.

Zygosity diagnosis in the absence of genotypic data: An approach using latent class analysis

For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

Establishing the next generation at work: Leader generativity as a moderator of the relationships between leader age, leader-member exchange, and leadership success

In this study, the authors investigated leader generativity as a moderator of the relationships between leader age, leader-member exchange, and three criteria of leadership success (follower perceptions of leader effectiveness, follower satisfaction with leader, and follower extra effort). Data came from 128 university professors paired with one research assistant each. Results showed positive relationships between leader age and leader generativity, and negative relationships between leader age and follower perceptions of leader effectiveness and follower extra effort. Consistent with expectations based on leadership categorization theory, leader generativity moderated the relationships between leader age and all three criteria of leadership success, such that leaders high in generativity were better able to maintain high levels of leadership success at higher ages than leaders low in generativity. Finally, results of mediated moderation analyses showed that leader-member exchange quality mediated these moderating effects. The findings suggest that, in combination, leader age and the age-related construct of generativity importantly influence leadership processes and outcomes. © 2011 American Psychological Association.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.