A study of the evolution of the representational capabilities of process modeling grammars

A plethora of process modeling techniques has been proposed over the years. One way of evaluating and comparing the scope and completeness of techniques is by way of representational analysis. The purpose of this paper is to examine how process modeling techniques have developed over the last four decades. The basis of the comparison is the Bunge-Wand-Weber representation model, a benchmark used for the analysis of grammars that purport to model the real world and the interactions within it. This paper presents a comparison of representational analyses of several popular process modeling techniques and has two main outcomes. First, it provides insights, within the boundaries of a representational analysis, into the extent to which process modeling techniques have developed over time. Second, the findings also indicate areas in which the underlying theory seems to be over-engineered or lacking in specialization.

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Representational Difference Analysis (RDA) reveals differential expression of conserved as well as novel genes during caste-specific development of the honey bee (Apis mellifera L.) ovary

In highly eusocial insects, such as the honey bee, Apis mellifera, the reproductive bias has become embedded in morphological caste differences. These are most expressively denoted in ovary size, with adult queens having large ovaries consisting of 150-200 ovarioles each, while workers typically have only 1-20 ovarioles per ovary. This morphological differentiation is a result of hormonal signals triggered by the diet change in the third larval instar, which eventually generate caste-specific gene expression patterns. To reveal these we produced differential gene expression libraries by Representational Difference Analysis (RDA) for queen and worker ovaries in a developmental stage when cell death is a prominent feature in the ovarioles of workers, whereas all ovarioles are maintained and extend in length in queens. In the queen library, 48% of the gene set represented homologs of known Drosophila genes, whereas in the worker ovary, the largest set (59%) were ESTs evidencing novel genes, not even computationally predicted in the honey bee genome. Differential expression was confirmed by quantitative RT-PCR for a selected gene set, denoting major differences for two queen and two worker library genes. These included two unpredicted genes located in chromosome 11 (Group11.35 and Group11.31, respectively) possibly representing long non-coding RNAs. Being candidates as modulators of ovary development, their expression and functional analysis should be a focal point for future studies. (C) 2011 Elsevier Ltd. All rights reserved.

Differential gene expression by Paracoccidioides brasiliensis in host interaction conditions: Representational difference analysis identifies candidate genes associated with fungal pathogenesis

Paracoccidioides brasiliensis causes infection by the host inhalation of airborne propagules of the mycelia phase of the fungus. These particles reach the lungs, and disseminate to virtually all organs. Here we describe the identification of differentially expressed genes in studies of host-fungus interaction. We analyzed two cDNA populations of P. brasiliensis, one obtained from infected animals and the other an admixture of fungus and human blood thus mimicking the hematologic events of the fungal dissemination. Our analysis identified transcripts differentially expressed. Genes related to iron acquisition, melanin synthesis and cell defense were specially upregulated in the mouse model of infection. The upregulated transcripts of yeast cells during incubation with human blood were those predominantly related to cell wall remodeling/synthesis. The expression pattern of genes was independently confirmed in host conditions, revealing their potential role in the infection process. This work can facilitate functional studies of novel regulated genes that may be important for the survival and growth strategies of P. brasiliensis in humans. (c) 2006 Elsevier Masson SAS. All rights reserved.

Análise por técnica de RDA (Representational Difference Analysis) da expressão gênica diferencial, para a identificação de fatores genéticos associados à produção de etanol em linhagem de Saccharomyces cerevisae

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

cDNA representational difference analysis used in the identification of genes expressed by Trichophyton rubrum during contact with keratin

Dermatophytes are adapted to infect skin, hair and nails by their ability to utilize keratin as a nutrient source. Trichophyton rubrum is an anthropophilic fungus, causing up to 90% of chronic cases of dermatophytosis. The understanding of the complex interactions between the fungus and its host should include the identification of genes expressed during infection. To identify the genes involved in the infection process, representational difference analysis (RDA) was applied to two cDNA populations from T. rubrum, one transcribed from the RNA of fungus cultured in the presence of keratin and the other from RNA generated during fungal growth in minimal medium. The analysis identified differentially expressed transcripts. Genes related to signal transduction, membrane protein, oxidative stress response, and some putative virulence factors were up-regulated during the contact of the fungus with keratin. The expression patterns of these genes were also verified by real-time PCR, in conidia of T. rubrum infecting primarily cultured human keratinocytes in vitro, revealing their potential role in the infective process. A better understanding of this interaction will contribute significantly to our knowledge of the process of dermatophyte infection.

A rat genetic map constructed by representational difference analysis markers with suitability for large-scale typing.

Representational difference analysis (RDA) was applied to isolate chromosomal markers in the rat. Four series of RDA [restriction enzymes, BamHI and HindIII; subtraction of ACI/N (ACI) amplicon from BUF/Nac (BUF) amplicon and vice versa] yielded 131 polymorphic markers; 125 of these markers were mapped to all chromosomes except for chromosome X. This was done by using a mapping panel of 105 ACI x BUF F2 rats. To complement the relative paucity of chromosomal markers in the rat, genetically directed RDA, which allows isolation of polymorphic markers in the specific chromosomal region, was performed. By changing the F2 driver-DNA allele frequency around the region, four markers were isolated from the D1Ncc1 locus. Twenty-five of 27 RDA markers were informative regarding the dot blot analysis of amplicons, hybridizing only with tester amplicons. Dot blot analysis at a high density per unit of area made it possible to process a large number of samples. Quantitative trait loci can now be mapped in the rat genome by processing a large number of samples with RDA markers and then by isolating markers close to the loci of interest by genetically directed RDA.

Isolation and characterization of a pseudoautosomal region-specific genetic marker in C57BL/6 mice using genomic representational difference analysis.

Representational difference analysis was used to identify strain-specific differences in the pseudoautosomal region (PAR) of mouse X and Y chromosomes. One second generation (C57BL/6 x Mus spretus) x Mus spretus interspecific backcross male carrying the C57BL/6 (B6) PAR was used for tester DNA. DNA from five backcross males from the same generation that were M. spretus-type for the PAR was pooled for the driver. A cloned probe designated B6-38 was recovered that is B6-specific in Southern analysis. Analysis of genomic DNA from several inbred strains of laboratory mice and diverse Mus species and subspecies identified a characteristic Pst I pattern of fragment sizes that is present only in the C57BL family of strains. Hybridization was observed with sequences in DBA/2J and to a limited extent with Mus musculus (PWK strain) and Mus castaneus DNA. No hybridization was observed in DNA of different Mus species, M. spretus, M. hortulanus, and M. caroli. Genetic analyses of B6-38 was conducted using C57BL congenic males that carry M. spretus alleles for distal X chromosome loci and the PAR and outcrosses of heterozygous congenic females with M. spretus. These analyses demonstrated that the B6-38 sequences were inherited with both the X and Y chromosome. B6-38 sequences were genetically mapped as a locus within the PAR using two interspecific backcrosses. The locus defined by B6-38 is designated DXYRp1. Preliminary analyses of recombination between the distal X chromosome gene amelogenin (Amg) and the PAR loci for either TelXY or sex chromosome association (Sxa) suggest that the locus DXYRp1 maps to the distal portion of the PAR.

Identification by representational difference analysis of a homozygous deletion in pancreatic carcinoma that lies within the BRCA2 region.

Homozygous deletions have been central to the discovery of several tumor-suppressor genes, but their finding has often been either serendipitous or the result of a directed search. A recently described technique [Lisitsyn, N., Lisitsyn, N. & Wigler, M. (1993) Science 259, 946-951] held out the potential to efficiently discover such events in an unbiased manner. Here we present the application of the representational difference analysis (RDA) to the study of cancer. We cloned two DNA fragments that identified a homozygous deletion in a human pancreatic adenocarcinoma, mapping to a 1-centimorgan region at chromosome 13q12.3 flanked by the markers D13S171 and D13S260. Interestingly, this lies within the 6-centimorgan region recently identified as the BRCA2 locus of heritable breast cancer susceptibility. This suggests that the same gene may be involved in multiple tumor types and that its function is that of a tumor suppressor rather than that of a dominant oncogene.

Representational difference analysis:Critical appraisal and method development for the identification of unique DNA sequences from prokaryotes

Representational difference analysis (RDA) has great potential for preferential amplification of unique but uncharacterised DNA sequences present in one source such as a whole genome, but absent from a related genome or other complex population of sequences. While a few examples of its successful exploitation have been published, the method has not been well dissected and robust, detailed published protocols are lacking. Here we examine the method in detail, suggest improvements and provide a protocol that has yielded key unique sequences from a pathogenic bacterial genome. © 2003 Elsevier Science B.V. All rights reserved.

Comparative transcriptome analysis of Paracoccidioides brasiliensis during in vitro adhesion to type I collagen and fibronectin: identification of potential adhesins

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Simbrain: A visual framework for neural network analysis and education

Simbrain is a visually-oriented framework for building and analyzing neural networks. It emphasizes the analysis of networks which control agents embedded in virtual environments, and visualization of the structures which occur in the high dimensional state spaces of these networks. The program was originally intended to facilitate analysis of representational processes in embodied agents, however it is also well suited to teaching neural networks concepts to a broader audience than is traditional for neural networks courses. Simbrain was used to teach a course at a new university, UC Merced, in its inaugural year. Experiences from the course and sample lessons are provided.