A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

This paper proposes to use a knowledge acquisition (KA) approach based on Nested Ripple Down Rules(NRDR) to assist in mechanical design focusing on dimensional tolerancing. A knowledge approach to incrementally model expert design processes is implemented. The knowledge is acquired in the context of its use, which substantially supports the KA process. The knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards in order to demonstrate the presented approach. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of theirdesigns in the future

COMODO: an adaptive coclustering strategy to identify conserved coexpression modules between organisms

Increasingly large-scale expression compendia for different species are becoming available. By exploiting the modularity of the coexpression network, these compendia can be used to identify biological processes for which the expression behavior is conserved over different species. However, comparing module networks across species is not trivial. The definition of a biologically meaningful module is not a fixed one and changing the distance threshold that defines the degree of coexpression gives rise to different modules. As a result when comparing modules across species, many different partially overlapping conserved module pairs across species exist and deciding which pair is most relevant is hard. Therefore, we developed a method referred to as conserved modules across organisms (COMODO) that uses an objective selection criterium to identify conserved expression modules between two species. The method uses as input microarray data and a gene homology map and provides as output pairs of conserved modules and searches for the pair of modules for which the number of sharing homologs is statistically most significant relative to the size of the linked modules. To demonstrate its principle, we applied COMODO to study coexpression conservation between the two well-studied bacteria Escherichia coli and Bacillus subtilis. COMODO is available at: http://homes.esat.kuleuven.be/∼kmarchal/Supplementary_Information_Zarrineh_2010/comodo/index.html

Learning and discovery in incremental knowledge acquisition

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required

Genome-scale co-expression network comparison across escherichia coli and salmonella enterica serovar typhimurium reveals significant conservation at the regulon level of local regulators despite their dissimilar lifestyles

Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica

Towards an assessment framework of reuse: A Knowledge Level Analysis Approach

The process of assessing the suitability of reuse of a software component is complex. Indeed, software systems are typically developed as an assembly of existing components. The complexity of the assessment process is due to lack of clarity on how to compare the cost of adaptation of an existing component versus the cost of developing it from scratch. Indeed, often pursuit of reuse can lead to excessive rework and adaptation, or developing suites of components that often get neglected. This paper is an important step towards modelling the complex reuse assessment process. To assess the success factors that can underpin reuse, we analyze the cognitive factors that belie developers\u27 behavior during their decision-making when attempting to reuse. This analysis is the first building block of a broader aim to synthesize a framework to institute activities during the software development lifecycle to support reuse

Function and expression of class I ribonucleotide reductase small subunit-encoding genes in Mycobacterium tuberculosis and Mycobacterium smegmatis

Ribonucleotide reductases (RNRs) are a class of enzymes catalyzing the de novo reduction of ribonucleotides to deoxyribonucleotides essential for DNA replication and repair. In addition to the class Ib RNR encoding genes, nrdE and nrdF2, Mycobacterium tuberculosis and Mycobacterium smegmatis also contain a homologue of a Chlamydial class Ic small subunit-encoding gene, nrdB. M. tuberculosis also contains an alternate class Ib RNR small (R2) subunit, NrdF1. In M. smegmatis mc2155, the class Ib RNR genes are located on a large chromosomal duplication. M. tuberculosis nrdF2 has been previously demonstrated to be essential for in vitro growth. It was hypothesized that different class I RNR R2 subunits could be used by the tubercle bacilli to survive and persist in the host. To test this hypothesis, function and expression of the class I R2-encoding genes in M. tuberculosis and M. smegmatis was investigated. Arguing against a specialist role for the alternate R2 subunits was the finding that NrdB in both organisms and NrdF1 in M. tuberculosis are individually and collectively dispensable for growth and long-term survival in vitro, resistance to genotoxic stress, adaptation during RNR inhibition by hydroxyurea and virulence in mice. Further confirming the essentiality of NrdF2 in mycobacteria and that NrdB cannot substitute for NrdF2 function in vitro was the finding that nrdF2 is essential for growth of a strain of M. smegmatis mc2155 lacking the duplicated chromosomal region ( DRKIN). DRKIN showed marked hypersensitivity to a wide range of compounds including hydroxyurea and mitomycin C, whereas deletion of only one copy of nrdF2 in M. smegmatis mc2155 resulted in a specific hypersensitivity to hydroxyurea. Through the construction of nrdR-deficient mutants of M. tuberculosis and M. smegmatis, the class Ib RNR genes were shown to be specifically regulated by an NrdR-type repressor, as evidenced by increase in nrdE and nrdF2 transcript levels in nrdR-deficient mutants of M. tuberculosis and M. smegmatis. Interestingly, however, upregulation of these genes did not affect M. smegmatis or M. tuberculosis in vitro growth, DNA damage survival or resistance to hydroxyurea. Together, these observations identify a potential vulnerability in dNTP provision in mycobacteria, and thereby offer a compelling rationale for pursuing the class Ib RNR as a target for drug discovery

The ballarat incremental knowledge engine

Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg