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Sample records for genome-scale identification method

  1. Genome-scale identification method applied to find cryptic aminoglycoside resistance genes in Pseudomonas aeruginosa.

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    Julie M Struble

    Full Text Available BACKGROUND: The ability of bacteria to rapidly evolve resistance to antibiotics is a critical public health problem. Resistance leads to increased disease severity and death rates, as well as imposes pressure towards the discovery and development of new antibiotic therapies. Improving understanding of the evolution and genetic basis of resistance is a fundamental goal in the field of microbiology. RESULTS: We have applied a new genomic method, Scalar Analysis of Library Enrichments (SCALEs, to identify genomic regions that, given increased copy number, may lead to aminoglycoside resistance in Pseudomonas aeruginosa at the genome scale. We report the result of selections on highly representative genomic libraries for three different aminoglycoside antibiotics (amikacin, gentamicin, and tobramycin. At the genome-scale, we show significant (p<0.05 overlap in genes identified for each aminoglycoside evaluated. Among the genomic segments identified, we confirmed increased resistance associated with an increased copy number of several genomic regions, including the ORF of PA5471, recently implicated in MexXY efflux pump related aminoglycoside resistance, PA4943-PA4946 (encoding a probable GTP-binding protein, a predicted host factor I protein, a delta 2-isopentenylpyrophosphate transferase, and DNA mismatch repair protein mutL, PA0960-PA0963 (encoding hypothetical proteins, a probable cold shock protein, a probable DNA-binding stress protein, and aspartyl-tRNA synthetase, a segment of PA4967 (encoding a topoisomerase IV subunit B, as well as a chimeric clone containing two inserts including the ORFs PA0547 and PA2326 (encoding a probable transcriptional regulator and a probable hypothetical protein, respectively. CONCLUSIONS: The studies reported here demonstrate the application of new a genomic method, SCALEs, which can be used to improve understanding of the evolution of antibiotic resistance in P. aeruginosa. In our demonstration studies, we

  2. Identification of novel targets for breast cancer by exploring gene switches on a genome scale

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    Wu Ming

    2011-11-01

    Full Text Available Abstract Background An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches. Results We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2 is uncovered. We further suggest a likely transcription factor that regulates TACSTD2. Conclusions Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

  3. Pantograph: A template-based method for genome-scale metabolic model reconstruction.

    Science.gov (United States)

    Loira, Nicolas; Zhukova, Anna; Sherman, David James

    2015-04-01

    Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr.

  4. SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria.

    Science.gov (United States)

    Krishnakumar, S; Durai, Dilip A; Wangikar, Pramod P; Viswanathan, Ganesh A

    2013-11-01

    Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.

  5. A Method to Constrain Genome-Scale Models with 13C Labeling Data.

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    Héctor García Martín

    2015-09-01

    Full Text Available Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA. This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

  6. Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Young, Jamey D.; Xu, Sibei

    2016-01-01

    Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade...... distributions (MIDs),(1) it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications......-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core...

  7. Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale.

    Science.gov (United States)

    McCloskey, Douglas; Young, Jamey D; Xu, Sibei; Palsson, Bernhard O; Feist, Adam M

    2016-04-05

    Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core pathways of traditional MFA models and also covers the additional pathways of purine, pyrimidine, isoprenoid, methionine, riboflavin, coenzyme A, and folate, as well as other biosynthetic pathways. When evaluating the iDM2014 using a set of measured intracellular intermediate and cofactor mass isotopomer distributions (MIDs),1 it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications such as the design of more complex bioprocessing strains and aid in identifying new antimicrobials. Importantly, it was found that there was no loss in precision of core fluxes when compared to a traditional core model, and additionally there was an overall increase in precision when considering all observable reactions.

  8. Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

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    David Burstein

    2009-07-01

    Full Text Available A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine

  9. Comparison of different cell type correction methods for genome-scale epigenetics studies.

    Science.gov (United States)

    Kaushal, Akhilesh; Zhang, Hongmei; Karmaus, Wilfried J J; Ray, Meredith; Torres, Mylin A; Smith, Alicia K; Wang, Shu-Li

    2017-04-14

    confounding is present, and FaST-LMM-EWASher gave the lowest sensitivity but highest specificity. Results from real data and simulations indicated that SVA is recommended when the focus is on the identification of informative CpGs. When appropriate reference data are available, the method implemented in the minfi package is recommended. However, if no such reference data are available or if the focus is not on estimating cell proportions, the SVA method is suggested.

  10. An effective method for controlling false discovery and false nondiscovery rates in genome-scale RNAi screens.

    Science.gov (United States)

    Zhang, Xiaohua Douglas

    2010-10-01

    In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.

  11. A genome-scale CRISPR-Cas9 screening method for protein stability reveals novel regulators of Cdc25A.

    Science.gov (United States)

    Wu, Yuanzhong; Zhou, Liwen; Wang, Xin; Lu, Jinping; Zhang, Ruhua; Liang, Xiaoting; Wang, Li; Deng, Wuguo; Zeng, Yi-Xin; Huang, Haojie; Kang, Tiebang

    2016-01-01

    The regulation of stability is particularly crucial for unstable proteins in cells. However, a convenient and unbiased method of identifying regulators of protein stability remains to be developed. Recently, a genome-scale CRISPR-Cas9 library has been established as a genetic tool to mediate loss-of-function screening. Here, we developed a protein stability regulators screening assay (Pro-SRSA) by combining the whole-genome CRISPR-Cas9 library with a dual-fluorescence-based protein stability reporter and high-throughput sequencing to screen for regulators of protein stability. Using Cdc25A as an example, Cul4B-DDB1(DCAF8) was identified as a new E3 ligase for Cdc25A. Moreover, the acetylation of Cdc25A at lysine 150, which was acetylated by p300/CBP and deacetylated by HDAC3, prevented the ubiquitin-mediated degradation of Cdc25A by the proteasome. This is the first study to report that acetylation, as a novel posttranslational modification, modulates Cdc25A stability, and we suggest that this unbiased CRISPR-Cas9 screening method at the genome scale may be widely used to globally identify regulators of protein stability.

  12. Genome-Scale Models

    DEFF Research Database (Denmark)

    Bergdahl, Basti; Sonnenschein, Nikolaus; Machado, Daniel

    2016-01-01

    An introduction to genome-scale models, how to build and use them, will be given in this chapter. Genome-scale models have become an important part of systems biology and metabolic engineering, and are increasingly used in research, both in academica and in industry, both for modeling chemical pr...

  13. In silico method for modelling metabolism and gene product expression at genome scale

    Energy Technology Data Exchange (ETDEWEB)

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

    2012-07-03

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

  14. Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis

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    Wang Shan

    2012-08-01

    Full Text Available Abstract Background Identification of the novel genes relevant to plant cell-wall (PCW synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. Results Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods. Conclusions We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html.

  15. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling

    NARCIS (Netherlands)

    Chaudhary, N.; Tøndel, K.; Bhatnagar, R.; Martins dos Santos, V.A.P.; Puchalka, J.

    2016-01-01

    Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential nov

  16. SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

    KAUST Repository

    Fan, Ming

    2012-06-28

    With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

  17. Meta-Analysis of Heterogeneous Data Sources for Genome-Scale Identification of Risk Genes in Complex Phenotypes

    DEFF Research Database (Denmark)

    Pers, Tune Hannes; Hansen, Niclas Tue; Hansen, Kasper Lage;

    2011-01-01

    Meta‐analyses of large‐scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome‐wide association (GWA) studies, protein......) with an odds ratio of 1.28 [1.12–1.48], which replicates a previous case‐control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available...

  18. DNA sequence recognition by hybridization to short oligomers : experimental determination of accuracy of the method in a genome-scale search.

    Energy Technology Data Exchange (ETDEWEB)

    Milosavljevic, A.; Savkovic, S.; Crkvenjakov, R.; Salbego, D.; Serrato, H.; Kreuzer, H.; Gemmell, A.; Batus, S.; Grujic, D.; Carnahan, S.; Tepavcevic, J.; Center for Mechanistic Biology and Biotechnology

    1996-01-01

    Recently developed hybridization technology enables economical large-scale detection of short oligomers within DNA fragments. The newly developed recognition method enables comparison of lists of oligomers detected within DNA fragments against known DNA sequences. We here describe an experiment involving a set of 4513 distinct genomic E. coli clones of average length 2kb, each hybridized with 636 randomly selected short oligomer probes. High hybridization signal with a particular probe was used as an indication of the presence of a complementary oligomer in the particular clone. For each clone, a list of oligomers with highest hybridization signals was compiled. The database consisting of 4513 oligomer lists was then searched using known E. coli sequences as queries in an attempt to identify the clones that match the query sequence. Out of a total of 11 clones that were recognized at highest significance level by our method, 8 were single-pass sequenced from both ends. The single-pass sequenced ends were then compared against the query sequences. The sequence comparisons confirmed 7 out of the total of 8 examined recognitions. This experiment represents the first successful example of genome-scale sequence recognition based on hybridization data.

  19. DNAshape: a method for the high-throughput prediction of DNA structural features on a genomic scale

    Science.gov (United States)

    Zhou, Tianyin; Yang, Lin; Lu, Yan; Dror, Iris; Dantas Machado, Ana Carolina; Ghane, Tahereh; Di Felice, Rosa; Rohs, Remo

    2013-01-01

    We present a method and web server for predicting DNA structural features in a high-throughput (HT) manner for massive sequence data. This approach provides the framework for the integration of DNA sequence and shape analyses in genome-wide studies. The HT methodology uses a sliding-window approach to mine DNA structural information obtained from Monte Carlo simulations. It requires only nucleotide sequence as input and instantly predicts multiple structural features of DNA (minor groove width, roll, propeller twist and helix twist). The results of rigorous validations of the HT predictions based on DNA structures solved by X-ray crystallography and NMR spectroscopy, hydroxyl radical cleavage data, statistical analysis and cross-validation, and molecular dynamics simulations provide strong confidence in this approach. The DNAshape web server is freely available at http://rohslab.cmb.usc.edu/DNAshape/. PMID:23703209

  20. Multicriteria identification sets method

    Science.gov (United States)

    Kamenev, G. K.

    2016-11-01

    A multicriteria identification and prediction method for mathematical models of simulation type in the case of several identification criteria (error functions) is proposed. The necessity of the multicriteria formulation arises, for example, when one needs to take into account errors of completely different origins (not reducible to a single characteristic) or when there is no information on the class of noise in the data to be analyzed. An identification sets method is described based on the approximation and visualization of the multidimensional graph of the identification error function and sets of suboptimal parameters. This method allows for additional advantages of the multicriteria approach, namely, the construction and visual analysis of the frontier and the effective identification set (frontier and the Pareto set for identification criteria), various representations of the sets of Pareto effective and subeffective parameter combinations, and the corresponding predictive trajectory tubes. The approximation is based on the deep holes method, which yields metric ɛ-coverings with nearly optimal properties, and on multiphase approximation methods for the Edgeworth-Pareto hull. The visualization relies on the approach of interactive decision maps. With the use of the multicriteria method, multiple-choice solutions of identification and prediction problems can be produced and justified by analyzing the stability of the optimal solution not only with respect to the parameters (robustness with respect to data) but also with respect to the chosen set of identification criteria (robustness with respect to the given collection of functionals).

  1. Using Genome-scale Models to Predict Biological Capabilities

    DEFF Research Database (Denmark)

    O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular...... growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started....

  2. Genome scale engineering techniques for metabolic engineering.

    Science.gov (United States)

    Liu, Rongming; Bassalo, Marcelo C; Zeitoun, Ramsey I; Gill, Ryan T

    2015-11-01

    Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.

  3. New statistical Methods of Genome-Scale Data Analysis in Life Science - Applications to enterobacterial Diagnostics, Meta-Analysis of Arabidopsis thaliana Gene Expression and functional Sequence Annotation

    OpenAIRE

    Friedrich, Torben

    2009-01-01

    Recent progresses and developments in molecular biology provide a wealth of new but insufficiently characterised data. This fund comprises amongst others biological data of genomic DNA, protein sequences, 3-dimensional protein structures as well as profiles of gene expression. In the present work, this information is used to develop new methods for the characterisation and classification of organisms and whole groups of organisms as well as to enhance the automated gain and transfer of inform...

  4. Fixing Formalin: A Method to Recover Genomic-Scale DNA Sequence Data from Formalin-Fixed Museum Specimens Using High-Throughput Sequencing

    Science.gov (United States)

    Hykin, Sarah M.; Bi, Ke; McGuire, Jimmy A.

    2015-01-01

    For 150 years or more, specimens were routinely collected and deposited in natural history collections without preserving fresh tissue samples for genetic analysis. In the case of most herpetological specimens (i.e. amphibians and reptiles), attempts to extract and sequence DNA from formalin-fixed, ethanol-preserved specimens—particularly for use in phylogenetic analyses—has been laborious and largely ineffective due to the highly fragmented nature of the DNA. As a result, tens of thousands of specimens in herpetological collections have not been available for sequence-based phylogenetic studies. Massively parallel High-Throughput Sequencing methods and the associated bioinformatics, however, are particularly suited to recovering meaningful genetic markers from severely degraded/fragmented DNA sequences such as DNA damaged by formalin-fixation. In this study, we compared previously published DNA extraction methods on three tissue types subsampled from formalin-fixed specimens of Anolis carolinensis, followed by sequencing. Sufficient quality DNA was recovered from liver tissue, making this technique minimally destructive to museum specimens. Sequencing was only successful for the more recently collected specimen (collected ~30 ybp). We suspect this could be due either to the conditions of preservation and/or the amount of tissue used for extraction purposes. For the successfully sequenced sample, we found a high rate of base misincorporation. After rigorous trimming, we successfully mapped 27.93% of the cleaned reads to the reference genome, were able to reconstruct the complete mitochondrial genome, and recovered an accurate phylogenetic placement for our specimen. We conclude that the amount of DNA available, which can vary depending on specimen age and preservation conditions, will determine if sequencing will be successful. The technique described here will greatly improve the value of museum collections by making many formalin-fixed specimens available for

  5. Fixing Formalin: A Method to Recover Genomic-Scale DNA Sequence Data from Formalin-Fixed Museum Specimens Using High-Throughput Sequencing.

    Science.gov (United States)

    Hykin, Sarah M; Bi, Ke; McGuire, Jimmy A

    2015-01-01

    For 150 years or more, specimens were routinely collected and deposited in natural history collections without preserving fresh tissue samples for genetic analysis. In the case of most herpetological specimens (i.e. amphibians and reptiles), attempts to extract and sequence DNA from formalin-fixed, ethanol-preserved specimens-particularly for use in phylogenetic analyses-has been laborious and largely ineffective due to the highly fragmented nature of the DNA. As a result, tens of thousands of specimens in herpetological collections have not been available for sequence-based phylogenetic studies. Massively parallel High-Throughput Sequencing methods and the associated bioinformatics, however, are particularly suited to recovering meaningful genetic markers from severely degraded/fragmented DNA sequences such as DNA damaged by formalin-fixation. In this study, we compared previously published DNA extraction methods on three tissue types subsampled from formalin-fixed specimens of Anolis carolinensis, followed by sequencing. Sufficient quality DNA was recovered from liver tissue, making this technique minimally destructive to museum specimens. Sequencing was only successful for the more recently collected specimen (collected ~30 ybp). We suspect this could be due either to the conditions of preservation and/or the amount of tissue used for extraction purposes. For the successfully sequenced sample, we found a high rate of base misincorporation. After rigorous trimming, we successfully mapped 27.93% of the cleaned reads to the reference genome, were able to reconstruct the complete mitochondrial genome, and recovered an accurate phylogenetic placement for our specimen. We conclude that the amount of DNA available, which can vary depending on specimen age and preservation conditions, will determine if sequencing will be successful. The technique described here will greatly improve the value of museum collections by making many formalin-fixed specimens available for

  6. Fixing Formalin: A Method to Recover Genomic-Scale DNA Sequence Data from Formalin-Fixed Museum Specimens Using High-Throughput Sequencing.

    Directory of Open Access Journals (Sweden)

    Sarah M Hykin

    Full Text Available For 150 years or more, specimens were routinely collected and deposited in natural history collections without preserving fresh tissue samples for genetic analysis. In the case of most herpetological specimens (i.e. amphibians and reptiles, attempts to extract and sequence DNA from formalin-fixed, ethanol-preserved specimens-particularly for use in phylogenetic analyses-has been laborious and largely ineffective due to the highly fragmented nature of the DNA. As a result, tens of thousands of specimens in herpetological collections have not been available for sequence-based phylogenetic studies. Massively parallel High-Throughput Sequencing methods and the associated bioinformatics, however, are particularly suited to recovering meaningful genetic markers from severely degraded/fragmented DNA sequences such as DNA damaged by formalin-fixation. In this study, we compared previously published DNA extraction methods on three tissue types subsampled from formalin-fixed specimens of Anolis carolinensis, followed by sequencing. Sufficient quality DNA was recovered from liver tissue, making this technique minimally destructive to museum specimens. Sequencing was only successful for the more recently collected specimen (collected ~30 ybp. We suspect this could be due either to the conditions of preservation and/or the amount of tissue used for extraction purposes. For the successfully sequenced sample, we found a high rate of base misincorporation. After rigorous trimming, we successfully mapped 27.93% of the cleaned reads to the reference genome, were able to reconstruct the complete mitochondrial genome, and recovered an accurate phylogenetic placement for our specimen. We conclude that the amount of DNA available, which can vary depending on specimen age and preservation conditions, will determine if sequencing will be successful. The technique described here will greatly improve the value of museum collections by making many formalin-fixed specimens

  7. Method of Fingerprint Identification

    OpenAIRE

    Surachai Panich

    2010-01-01

    Problem statement: The main task of this study was finding out an effective algorithm in order to match two fingerprints taken from database. The fingerprint identification techniques used in a small database in order to find out an effective algorithm to develop the accuracy in matching process. Higher-level application of this proposed algorithm was determined. Approach: The main objectives in this study were the construction of fingerprint database and matching algorithm for comparison of ...

  8. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2016-01-01

    been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...... of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome...

  9. Next-generation genome-scale models for metabolic engineering

    DEFF Research Database (Denmark)

    King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed. -. encompassing many biological processes and simulation strategies. -. and next-generation models enable new types of predictions. Here, three key...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

  10. Next-generation genome-scale models for metabolic engineering.

    Science.gov (United States)

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering.

  11. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  12. Genome-scale engineering for systems and synthetic biology

    Science.gov (United States)

    Esvelt, Kevin M; Wang, Harris H

    2013-01-01

    Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review current technologies and methodologies for genome-scale engineering, discuss the prospects for extending efficient genome modification to new hosts, and explore the implications of continued advances toward the development of flexibly programmable chasses, novel biochemistries, and safer organismal and ecological engineering. PMID:23340847

  13. Genome-scale engineering for systems and synthetic biology.

    Science.gov (United States)

    Esvelt, Kevin M; Wang, Harris H

    2013-01-01

    Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review current technologies and methodologies for genome-scale engineering, discuss the prospects for extending efficient genome modification to new hosts, and explore the implications of continued advances toward the development of flexibly programmable chasses, novel biochemistries, and safer organismal and ecological engineering.

  14. Modeling cancer metabolism on a genome scale

    Science.gov (United States)

    Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

    2015-01-01

    Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

  15. Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

    DEFF Research Database (Denmark)

    Österlund, Tobias; Nookaew, Intawat; Bordel, Sergio

    2013-01-01

    ABSTRACT: BACKGROUND: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. RESULTS......: Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap......-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold...

  16. Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

    Science.gov (United States)

    Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

    2016-06-08

    Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named LTM (logical transformation of model) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

  17. New omega vortex identification method

    Science.gov (United States)

    Liu, ChaoQun; Wang, YiQian; Yang, Yong; Duan, ZhiWei

    2016-08-01

    A new vortex identification criterion called Ω-method is proposed based on the ideas that vorticity overtakes deformation in vortex. The comparison with other vortex identification methods like Q-criterion and λ 2-method is conducted and the advantages of the new method can be summarized as follows: (1) the method is able to capture vortex well and very easy to perform; (2) the physical meaning of Ω is clear while the interpretations of iso-surface values of Q and λ 2 chosen to visualize vortices are obscure; (3) being different from Q and λ 2 iso-surface visualization which requires wildly various thresholds to capture the vortex structure properly, Ω is pretty universal and does not need much adjustment in different cases and the iso-surfaces of Ω=0.52 can always capture the vortices properly in all the cases at different time steps, which we investigated; (4) both strong and weak vortices can be captured well simultaneously while improper Q and λ 2 threshold may lead to strong vortex capture while weak vortices are lost or weak vortices are captured but strong vortices are smeared; (5) Ω=0.52 is a quantity to approximately define the vortex boundary. Note that, to calculate Ω, the length and velocity must be used in the non-dimensional form. From our direct numerical simulation, it is found that the vorticity direction is very different from the vortex rotation direction in general 3-D vortical flow, the Helmholtz velocity decomposition is reviewed and vorticity is proposed to be further decomposed to vortical vorticity and non-vortical vorticity.

  18. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  19. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

    Directory of Open Access Journals (Sweden)

    Hirasawa Takashi

    2009-08-01

    Full Text Available Abstract Background In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA, and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates. Results The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production. Conclusion The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

  20. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH

    NARCIS (Netherlands)

    Bienko, M.; Crosetto, N.; Teytelman, L.; Klemm, S.; Itzkovitz, S.; van Oudenaarden, A.

    2013-01-01

    We developed a cost-effective genome-scale PCR-based method for high-definition DNA FISH (HD-FISH). We visualized gene loci with diffraction-limited resolution, chromosomes as spot clusters and single genes together with transcripts by combining HD-FISH with single-molecule RNA FISH. We provide a da

  1. Genome-scale modeling of human metabolism - a systems biology approach.

    Science.gov (United States)

    Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

    2013-09-01

    Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models - one of the fundamental aspects of systems biology - have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Metingear: a development environment for annotating genome-scale metabolic models.

    Science.gov (United States)

    May, John W; James, A Gordon; Steinbeck, Christoph

    2013-09-01

    Genome-scale metabolic models often lack annotations that would allow them to be used for further analysis. Previous efforts have focused on associating metabolites in the model with a cross reference, but this can be problematic if the reference is not freely available, multiple resources are used or the metabolite is added from a literature review. Associating each metabolite with chemical structure provides unambiguous identification of the components and a more detailed view of the metabolism. We have developed an open-source desktop application that simplifies the process of adding database cross references and chemical structures to genome-scale metabolic models. Annotated models can be exported to the Systems Biology Markup Language open interchange format. Source code, binaries, documentation and tutorials are freely available at http://johnmay.github.com/metingear. The application is implemented in Java with bundles available for MS Windows and Macintosh OS X.

  3. Star identification methods, techniques and algorithms

    CERN Document Server

    Zhang, Guangjun

    2017-01-01

    This book summarizes the research advances in star identification that the author’s team has made over the past 10 years, systematically introducing the principles of star identification, general methods, key techniques and practicable algorithms. It also offers examples of hardware implementation and performance evaluation for the star identification algorithms. Star identification is the key step for celestial navigation and greatly improves the performance of star sensors, and as such the book include the fundamentals of star sensors and celestial navigation, the processing of the star catalog and star images, star identification using modified triangle algorithms, star identification using star patterns and using neural networks, rapid star tracking using star matching between adjacent frames, as well as implementation hardware and using performance tests for star identification. It is not only valuable as a reference book for star sensor designers and researchers working in pattern recognition and othe...

  4. The OME Framework for genome-scale systems biology

    Energy Technology Data Exchange (ETDEWEB)

    Palsson, Bernhard O. [Univ. of California, San Diego, CA (United States); Ebrahim, Ali [Univ. of California, San Diego, CA (United States); Federowicz, Steve [Univ. of California, San Diego, CA (United States)

    2014-12-19

    The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed [1-3]. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome- scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind [4]. It is also evident that, given the plethora of databases and software efforts [5-12], it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models [13-15]. We work at the forefront of model creation and systems scale data generation [16-18]. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities [13, 19-24] towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale

  5. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Directory of Open Access Journals (Sweden)

    Kevin J Tsai

    Full Text Available Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  6. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Science.gov (United States)

    Tsai, Kevin J; Chang, Chuan-Hsiung

    2014-01-01

    Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  7. Use of genome-scale microbial models for metabolic engineering

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

    2004-01-01

    network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.......Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic...

  8. Integration of expression data in genome-scale metabolic network reconstructions

    Directory of Open Access Journals (Sweden)

    Anna S. Blazier

    2012-08-01

    Full Text Available With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of omics data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA, a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

  9. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    Science.gov (United States)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online.

  10. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  11. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH.

    Science.gov (United States)

    Bienko, Magda; Crosetto, Nicola; Teytelman, Leonid; Klemm, Sandy; Itzkovitz, Shalev; van Oudenaarden, Alexander

    2013-02-01

    We developed a cost-effective genome-scale PCR-based method for high-definition DNA FISH (HD-FISH). We visualized gene loci with diffraction-limited resolution, chromosomes as spot clusters and single genes together with transcripts by combining HD-FISH with single-molecule RNA FISH. We provide a database of over 4.3 million primer pairs targeting the human and mouse genomes that is readily usable for rapid and flexible generation of probes.

  12. Rapid genome-scale mapping of chromatin accessibility in tissue

    DEFF Research Database (Denmark)

    Grøntved, Lars; Bandle, Russell; John, Sam;

    2012-01-01

    BACKGROUND: The challenge in extracting genome-wide chromatin features from limiting clinical samples poses a significant hurdle in identification of regulatory marks that impact the physiological or pathological state. Current methods that identify nuclease accessible chromatin are reliant...

  13. Proposed standard methods for ink identification.

    Science.gov (United States)

    Kelley, J D; Cantu, A A

    1975-01-01

    Two methods are presented for the identification of ink, both of which are currently being used by laboratories involved in ink analysis. Both methods incorporate physical and chemical procedures. The differences are primarily in chemical methods involving spot tests and thin layer chromatographic (TLC) techniques. Furthermore, one method utilizes spectrophotometric scanning of the TLC plate whereas the other method utilizes solution spectrophotometry. It is recommended that both methods be evaluated by several laboratories.

  14. Genome-scale modeling for metabolic engineering

    Energy Technology Data Exchange (ETDEWEB)

    Simeonidis, E; Price, ND

    2015-01-13

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

  15. Genome-scale modeling for metabolic engineering.

    Science.gov (United States)

    Simeonidis, Evangelos; Price, Nathan D

    2015-03-01

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

  16. Identification methods for structural health monitoring

    CERN Document Server

    Papadimitriou, Costas

    2016-01-01

    The papers in this volume provide an introduction to well known and established system identification methods for structural health monitoring and to more advanced, state-of-the-art tools, able to tackle the challenges associated with actual implementation. Starting with an overview on fundamental methods, introductory concepts are provided on the general framework of time and frequency domain, parametric and non-parametric methods, input-output or output only techniques. Cutting edge tools are introduced including, nonlinear system identification methods; Bayesian tools; and advanced modal identification techniques (such as the Kalman and particle filters, the fast Bayesian FFT method). Advanced computational tools for uncertainty quantification are discussed to provide a link between monitoring and structural integrity assessment. In addition, full scale applications and field deployments that illustrate the workings and effectiveness of the introduced monitoring schemes are demonstrated.

  17. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  18. Determination of sample size in genome-scale RNAi screens.

    Science.gov (United States)

    Zhang, Xiaohua Douglas; Heyse, Joseph F

    2009-04-01

    For genome-scale RNAi research, it is critical to investigate sample size required for the achievement of reasonably low false negative rate (FNR) and false positive rate. The analysis in this article reveals that current design of sample size contributes to the occurrence of low signal-to-noise ratio in genome-scale RNAi projects. The analysis suggests that (i) an arrangement of 16 wells per plate is acceptable and an arrangement of 20-24 wells per plate is preferable for a negative control to be used for hit selection in a primary screen without replicates; (ii) in a confirmatory screen or a primary screen with replicates, a sample size of 3 is not large enough, and there is a large reduction in FNRs when sample size increases from 3 to 4. To search a tradeoff between benefit and cost, any sample size between 4 and 11 is a reasonable choice. If the main focus is the selection of siRNAs with strong effects, a sample size of 4 or 5 is a good choice. If we want to have enough power to detect siRNAs with moderate effects, sample size needs to be 8, 9, 10 or 11. These discoveries about sample size bring insight to the design of a genome-scale RNAi screen experiment.

  19. Method for genetic identification of unknown organisms

    Energy Technology Data Exchange (ETDEWEB)

    Colston, Jr., Billy W.; Fitch, Joseph P.; Hindson, Benjamin J.; Carter, Chance J.; Beer, Neil Reginald

    2016-08-23

    A method of rapid, genome and proteome based identification of unknown pathogenic or non-pathogenic organisms in a complex sample. The entire sample is analyzed by creating millions of emulsion encapsulated microdroplets, each containing a single pathogenic or non-pathogenic organism sized particle and appropriate reagents for amplification. Following amplification, the amplified product is analyzed.

  20. Metingear: a development environment for annotating genome-scale metabolic models

    Science.gov (United States)

    May, John W.; James, A. Gordon; Steinbeck, Christoph

    2013-01-01

    Summary: Genome-scale metabolic models often lack annotations that would allow them to be used for further analysis. Previous efforts have focused on associating metabolites in the model with a cross reference, but this can be problematic if the reference is not freely available, multiple resources are used or the metabolite is added from a literature review. Associating each metabolite with chemical structure provides unambiguous identification of the components and a more detailed view of the metabolism. We have developed an open-source desktop application that simplifies the process of adding database cross references and chemical structures to genome-scale metabolic models. Annotated models can be exported to the Systems Biology Markup Language open interchange format. Availability: Source code, binaries, documentation and tutorials are freely available at http://johnmay.github.com/metingear. The application is implemented in Java with bundles available for MS Windows and Macintosh OS X. Contact: johnmay@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23766418

  1. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

    Science.gov (United States)

    Ataman, Meric; Hernandez Gardiol, Daniel F; Fengos, Georgios; Hatzimanikatis, Vassily

    2017-07-01

    Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

  2. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

    Directory of Open Access Journals (Sweden)

    Meric Ataman

    2017-07-01

    Full Text Available Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

  3. 13C metabolic flux analysis at a genome-scale.

    Science.gov (United States)

    Gopalakrishnan, Saratram; Maranas, Costas D

    2015-11-01

    Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non

  4. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE.

    Science.gov (United States)

    Wang, Yuliang; Eddy, James A; Price, Nathan D

    2012-12-13

    Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues. We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues. This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

  5. Genome-scale constraint-based modeling of Geobacter metallireducens

    Directory of Open Access Journals (Sweden)

    Famili Iman

    2009-01-01

    Full Text Available Abstract Background Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens. Results The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens' specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale

  6. New Waste Beverage Cans Identification Method

    Directory of Open Access Journals (Sweden)

    Firmansyah Burlian

    2016-05-01

    Full Text Available The primary emphasis of this work is on the development of a new waste beverage cans identification method for automated beverage cans sorting systems known as the SVS system. The method described involved window-based subdivision of the image into X-cells, construction of X-candidate template for N-cells, calculation of matching scores of reference templates for the N-cells image, and application of matching score to identify the grade of the object. The SVS system performance for correct beverage cans grade identification is 95.17% with estimated throughput of 21,600 objects per hour with a conveyor belt width of 18˝. The weight of the throughput depends on the size and type of the objects.

  7. Computational botany methods for automated species identification

    CERN Document Server

    Remagnino, Paolo; Wilkin, Paul; Cope, James; Kirkup, Don

    2017-01-01

    This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools fo...

  8. Genome Scale Transcriptomics of Baculovirus-Insect Interactions

    Directory of Open Access Journals (Sweden)

    Steven Reid

    2013-11-01

    Full Text Available Baculovirus-insect cell technologies are applied in the production of complex proteins, veterinary and human vaccines, gene delivery vectors‚ and biopesticides. Better understanding of how baculoviruses and insect cells interact would facilitate baculovirus-based production. While complete genomic sequences are available for over 58 baculovirus species, little insect genomic information is known. The release of the Bombyx mori and Plutella xylostella genomes, the accumulation of EST sequences for several Lepidopteran species, and especially the availability of two genome-scale analysis tools, namely oligonucleotide microarrays and next generation sequencing (NGS, have facilitated expression studies to generate a rich picture of insect gene responses to baculovirus infections. This review presents current knowledge on the interaction dynamics of the baculovirus-insect system‚ which is relatively well studied in relation to nucleocapsid transportation, apoptosis, and heat shock responses, but is still poorly understood regarding responses involved in pro-survival pathways, DNA damage pathways, protein degradation, translation, signaling pathways, RNAi pathways, and importantly metabolic pathways for energy, nucleotide and amino acid production. We discuss how the two genome-scale transcriptomic tools can be applied for studying such pathways and suggest that proteomics and metabolomics can produce complementary findings to transcriptomic studies.

  9. Current state of genome-scale modeling in filamentous fungi.

    Science.gov (United States)

    Brandl, Julian; Andersen, Mikael R

    2015-06-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.

  10. Genome scale transcriptomics of baculovirus-insect interactions.

    Science.gov (United States)

    Nguyen, Quan; Nielsen, Lars K; Reid, Steven

    2013-11-12

    Baculovirus-insect cell technologies are applied in the production of complex proteins, veterinary and human vaccines, gene delivery vectors' and biopesticides. Better understanding of how baculoviruses and insect cells interact would facilitate baculovirus-based production. While complete genomic sequences are available for over 58 baculovirus species, little insect genomic information is known. The release of the Bombyx mori and Plutella xylostella genomes, the accumulation of EST sequences for several Lepidopteran species, and especially the availability of two genome-scale analysis tools, namely oligonucleotide microarrays and next generation sequencing (NGS), have facilitated expression studies to generate a rich picture of insect gene responses to baculovirus infections. This review presents current knowledge on the interaction dynamics of the baculovirus-insect system' which is relatively well studied in relation to nucleocapsid transportation, apoptosis, and heat shock responses, but is still poorly understood regarding responses involved in pro-survival pathways, DNA damage pathways, protein degradation, translation, signaling pathways, RNAi pathways, and importantly metabolic pathways for energy, nucleotide and amino acid production. We discuss how the two genome-scale transcriptomic tools can be applied for studying such pathways and suggest that proteomics and metabolomics can produce complementary findings to transcriptomic studies.

  11. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    Science.gov (United States)

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  12. SPECIES IDENTIFICATION OF MEAT BY ELECTROPHORETIC METHODS

    Directory of Open Access Journals (Sweden)

    Edward Pospiech

    2007-03-01

    Full Text Available Electrophoretic methods can be used to identify meat of various animal species. The protein electrophoresis, especially the IEF of the sarcoplasmic proteins, is a well-established technique for species identification of raw fish and is used in the control of seafood authenticity. However, in the case of the analysis of heat-processed fish, the method is applicable only to those species which possess characteristic patterns of the heat-stable parvalbumins. Heat-denatured fish muscle proteins may be solubilised by urea or sodium dodecylsulfate (SDS and separated by urea-IEF or SDS-PAGE, respectively. The comparison of these two methods allowed to conclude that, basically, each of them can be used for species identification of heated fishery products. However, extensively washed products may be preferentially analysed by the SDS-PAGE, because most of the parvalbumins are washed out leaving mainly myosins. On the other hand, the IEF method may be preferred for the differentiation of closely related species rich in parvalbumins isoforms. It is evident from the literature data that species-specific protein separations yield proteins of low molecular weight made up of three light chains of myosin (14-23 kDa, troponin (19-30 kDa and parvalbumin (about 12 kDa. Investigations showed that the SDS-PAGE method can be used to identify meats of: cattle, sheep, lambs, goats, red deer and rabbits. The technique allowed researchers to identify the following myofibrillar and sarcoplasmic muscle proteins: myosin and actin, α-actinin, tropomyosin, troponin. SDS-PAGE allowed the identification of myofibrillar proteins taking into account their molecular weights which was not possible with the assistance of the PAGIF because too many protein bands were obtained. It was possible to obtain differences in the separation of proteins characteristic for certain species, e.g. beef, resulting from the presence of sin-gle myofibrillar proteins.

  13. Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models.

    Science.gov (United States)

    Hamilton, Joshua J; Dwivedi, Vivek; Reed, Jennifer L

    2013-07-16

    Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations.

  14. Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

    Science.gov (United States)

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

    Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

  15. Identification methods for nonlinear stochastic systems.

    Science.gov (United States)

    Fullana, Jose-Maria; Rossi, Maurice

    2002-03-01

    Model identifications based on orbit tracking methods are here extended to stochastic differential equations. In the present approach, deterministic and statistical features are introduced via the time evolution of ensemble averages and variances. The aforementioned quantities are shown to follow deterministic equations, which are explicitly written within a linear as well as a weakly nonlinear approximation. Based on such equations and the observed time series, a cost function is defined. Its minimization by simulated annealing or backpropagation algorithms then yields a set of best-fit parameters. This procedure is successfully applied for various sampling time intervals, on a stochastic Lorenz system.

  16. Genome-scale validation of deep-sequencing libraries.

    Directory of Open Access Journals (Sweden)

    Dominic Schmidt

    Full Text Available Chromatin immunoprecipitation followed by high-throughput (HTP sequencing (ChIP-seq is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing.

  17. Current state of genome-scale modeling in filamentous fungi

    DEFF Research Database (Denmark)

    Brandl, Julian; Andersen, Mikael Rørdam

    2015-01-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full...... testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique...... metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi....

  18. Modeling Lactococcus lactis using a genome-scale flux model

    Directory of Open Access Journals (Sweden)

    Nielsen Jens

    2005-06-01

    Full Text Available Abstract Background Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA and minimization of metabolic adjustment (MOMA were used as modeling frameworks. Results The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. Conclusion The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

  19. Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

    Science.gov (United States)

    Pitkänen, Esa; Jouhten, Paula; Hou, Jian; Syed, Muhammad Fahad; Blomberg, Peter; Kludas, Jana; Oja, Merja; Holm, Liisa; Penttilä, Merja; Rousu, Juho; Arvas, Mikko

    2014-01-01

    We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. PMID:24516375

  20. Informed Consent in Genome-Scale Research: What Do Prospective Participants Think?

    Science.gov (United States)

    Trinidad, Susan Brown; Fullerton, Stephanie M.; Bares, Julie M.; Jarvik, Gail P.; Larson, Eric B.; Burke, Wylie

    2012-01-01

    Background To promote effective genome-scale research, genomic and clinical data for large population samples must be collected, stored, and shared. Methods We conducted focus groups with 45 members of a Seattle-based integrated healthcare delivery system to learn about their views and expectations for informed consent in genome-scale studies. Results Participants viewed information about study purpose, aims, and how and by whom study data could be used to be at least as important as information about risks and possible harms. They generally supported a tiered consent approach for specific issues, including research purpose, data sharing, and access to individual research results. Participants expressed a continuum of opinions with respect to the acceptability of broad consent, ranging from completely acceptable to completely unacceptable. Older participants were more likely to view the consent process in relational – rather than contractual – terms, compared with younger participants. The majority of participants endorsed seeking study subjects’ permission regarding material changes in study purpose and data sharing. Conclusions Although this study sample was limited in terms of racial and socioeconomic diversity, our results suggest a strong positive interest in genomic research on the part of at least some prospective participants and indicate a need for increased public engagement, as well as strategies for ongoing communication with study participants. PMID:23493836

  1. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species.

    Directory of Open Access Journals (Sweden)

    Esa Pitkänen

    2014-02-01

    Full Text Available We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

  2. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

    Science.gov (United States)

    Chaudhary, Neha; Tøndel, Kristin; Bhatnagar, Rakesh; dos Santos, Vítor A P Martins; Puchałka, Jacek

    2016-03-01

    Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.

  3. Toward the automated generation of genome-scale metabolic networks in the SEED

    Directory of Open Access Journals (Sweden)

    Gould John

    2007-04-01

    Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative

  4. [A accurate identification method for Chinese materia medica--systematic identification of Chinese materia medica].

    Science.gov (United States)

    Wang, Xue-Yong; Liao, Cai-Li; Liu, Si-Qi; Liu, Chun-Sheng; Shao, Ai-Juan; Huang, Lu-Qi

    2013-05-01

    This paper put forward a more accurate identification method for identification of Chinese materia medica (CMM), the systematic identification of Chinese materia medica (SICMM) , which might solve difficulties in CMM identification used the ordinary traditional ways. Concepts, mechanisms and methods of SICMM were systematically introduced and possibility was proved by experiments. The establishment of SICMM will solve problems in identification of Chinese materia medica not only in phenotypic characters like the mnorphous, microstructure, chemical constituents, but also further discovery evolution and classification of species, subspecies and population in medical plants. The establishment of SICMM will improve the development of identification of CMM and create a more extensive study space.

  5. Progress of DNA-based Methods for Species Identification

    Institute of Scientific and Technical Information of China (English)

    HU Zhen; ZHANG Su-hua; WANG Zheng; BIAN Ying-nan; LI Cheng-tao

    2015-01-01

    Species identification of biological samples is widely used in such fields as forensic science and food industry. A variety of accurate and reliable methods have been developed in recent years. The cur-rent reviewshows common target genes and screening criteria suitable for species identification, and de-scribed various DNA-based molecular biology methods about species identification. Additionally, it dis-cusses the future development of species identification combined with real-time PCR and sequencing technologies.

  6. Supervised Classification Methods for Seismic Phase Identification

    Science.gov (United States)

    Schneider, Jeff; Given, Jeff; Le Bras, Ronan; Fisseha, Misrak

    2010-05-01

    The Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) is tasked with monitoring compliance with the CTBT. The organization is installing the International Monitoring System (IMS), a global network of seismic, hydroacoustic, infrasound, and radionuclide sensor stations. The International Data Centre (IDC) receives the data from seismic stations either in real time or on request. These data are first processed on a station per station basis. This initial step yields discrete detections which are then assembled on a network basis (with the addition of hydroacoustic and infrasound data) to produce automatic and analyst reviewed bulletins containing seismic, hydroacoustic, and infrasound detections. The initial station processing step includes the identification of seismic and acoustic phases which are given a label. Subsequent network processing relies on this preliminary labeling, and as a consequence, the accuracy and reliability of automatic and reviewed bulletins also depend on this initial step. A very large ground truth database containing massive amounts of detections with analyst-reviewed labels is available to improve on the current operational system using machine learning methods. An initial study using a limited amount of data was conducted during the ISS09 project of the CTBTO. Several classification methods were tested: decision tree with bagging; logistic regression; neural networks trained with back-propagation; Bayesian networks as generative class models; naive Bayse classification; support vector machines. The initial assessment was that the phase identification process could be improved by at least 13% over the current operational system and that the method obtaining the best results was the decision tree with bagging. We present the results of a study using a much larger learning dataset and preliminary implementation results.

  7. IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhiyi; FAN Jiangling; HUA Hongxing

    2006-01-01

    An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix. In combination with component energy index (CEI) which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio (SNR)and gives a reliable separation of spurious and physical estimates.

  8. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

    Directory of Open Access Journals (Sweden)

    McAnulty Michael J

    2012-05-01

    Full Text Available Abstract Background Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. Results A new method called “flux balance analysis with flux ratios (FBrAtio” was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490 that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i acetate, (ii lactate, (iii butyrate, (iv acetone, (v butanol, (vi ethanol, (vii CO2 and (viii H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. Conclusions FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

  9. A genomic scale map of genetic diversity in Trypanosoma cruzi

    Directory of Open Access Journals (Sweden)

    Ackermann Alejandro A

    2012-12-01

    Full Text Available Abstract Background Trypanosoma cruzi, the causal agent of Chagas Disease, affects more than 16 million people in Latin America. The clinical outcome of the disease results from a complex interplay between environmental factors and the genetic background of both the human host and the parasite. However, knowledge of the genetic diversity of the parasite, is currently limited to a number of highly studied loci. The availability of a number of genomes from different evolutionary lineages of T. cruzi provides an unprecedented opportunity to look at the genetic diversity of the parasite at a genomic scale. Results Using a bioinformatic strategy, we have clustered T. cruzi sequence data available in the public domain and obtained multiple sequence alignments in which one or two alleles from the reference CL-Brener were included. These data covers 4 major evolutionary lineages (DTUs: TcI, TcII, TcIII, and the hybrid TcVI. Using these set of alignments we have identified 288,957 high quality single nucleotide polymorphisms and 1,480 indels. In a reduced re-sequencing study we were able to validate ~ 97% of high-quality SNPs identified in 47 loci. Analysis of how these changes affect encoded protein products showed a 0.77 ratio of synonymous to non-synonymous changes in the T. cruzi genome. We observed 113 changes that introduce or remove a stop codon, some causing significant functional changes, and a number of tri-allelic and tetra-allelic SNPs that could be exploited in strain typing assays. Based on an analysis of the observed nucleotide diversity we show that the T. cruzi genome contains a core set of genes that are under apparent purifying selection. Interestingly, orthologs of known druggable targets show statistically significant lower nucleotide diversity values. Conclusions This study provides the first look at the genetic diversity of T. cruzi at a genomic scale. The analysis covers an estimated ~ 60% of the genetic diversity present in the

  10. [Bacterial identification methods in the microbiology laboratory].

    Science.gov (United States)

    Bou, Germán; Fernández-Olmos, Ana; García, Celia; Sáez-Nieto, Juan Antonio; Valdezate, Sylvia

    2011-10-01

    In order to identify the agent responsible of the infectious process and understanding the pathogenic/pathological implications, clinical course, and to implement an effective antimicrobial therapy, a mainstay in the practice of clinical microbiology is the allocation of species to a microbial isolation. In daily routine practice microbiology laboratory phenotypic techniques are applied to achieve this goal. However, they have some limitations that are seen more clearly for some kinds of microorganism. Molecular methods can circumvent some of these limitations, although its implementation is not universal. This is due to higher costs and the level of expertise required for thei implementation, so molecular methods are often centralized in reference laboratories and centers. Recently, proteomics-based methods made an important breakthrough in the field of diagnostic microbiology and will undoubtedly have a major impact on the future organization of the microbiology services. This paper is a short review of the most noteworthy aspects of the three bacterial identification methods described above used in microbiology laboratories. Copyright © 2011 Elsevier España, S.L. All rights reserved.

  11. Improved Evidence-Based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

    Energy Technology Data Exchange (ETDEWEB)

    Seaver, Samuel M.D.; Frelin, Oceane; Bradbury, Louis M.T.; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-03-10

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  12. Improved Evidence-Based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm.

    Directory of Open Access Journals (Sweden)

    Samuel eSeaver

    2015-03-01

    Full Text Available There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  13. Rapid genome-scale mapping of chromatin accessibility in tissue

    Science.gov (United States)

    2012-01-01

    Background The challenge in extracting genome-wide chromatin features from limiting clinical samples poses a significant hurdle in identification of regulatory marks that impact the physiological or pathological state. Current methods that identify nuclease accessible chromatin are reliant on large amounts of purified nuclei as starting material. This complicates analysis of trace clinical tissue samples that are often stored frozen. We have developed an alternative nuclease based procedure to bypass nuclear preparation to interrogate nuclease accessible regions in frozen tissue samples. Results Here we introduce a novel technique that specifically identifies Tissue Accessible Chromatin (TACh). The TACh method uses pulverized frozen tissue as starting material and employs one of the two robust endonucleases, Benzonase or Cyansase, which are fully active under a range of stringent conditions such as high levels of detergent and DTT. As a proof of principle we applied TACh to frozen mouse liver tissue. Combined with massive parallel sequencing TACh identifies accessible regions that are associated with euchromatic features and accessibility at transcriptional start sites correlates positively with levels of gene transcription. Accessible chromatin identified by TACh overlaps to a large extend with accessible chromatin identified by DNase I using nuclei purified from freshly isolated liver tissue as starting material. The similarities are most pronounced at highly accessible regions, whereas identification of less accessible regions tends to be more divergence between nucleases. Interestingly, we show that some of the differences between DNase I and Benzonase relate to their intrinsic sequence biases and accordingly accessibility of CpG islands is probed more efficiently using TACh. Conclusion The TACh methodology identifies accessible chromatin derived from frozen tissue samples. We propose that this simple, robust approach can be applied across a broad range of

  14. Rapid genome-scale mapping of chromatin accessibility in tissue

    Directory of Open Access Journals (Sweden)

    Grøntved Lars

    2012-06-01

    Full Text Available Abstract Background The challenge in extracting genome-wide chromatin features from limiting clinical samples poses a significant hurdle in identification of regulatory marks that impact the physiological or pathological state. Current methods that identify nuclease accessible chromatin are reliant on large amounts of purified nuclei as starting material. This complicates analysis of trace clinical tissue samples that are often stored frozen. We have developed an alternative nuclease based procedure to bypass nuclear preparation to interrogate nuclease accessible regions in frozen tissue samples. Results Here we introduce a novel technique that specifically identifies Tissue Accessible Chromatin (TACh. The TACh method uses pulverized frozen tissue as starting material and employs one of the two robust endonucleases, Benzonase or Cyansase, which are fully active under a range of stringent conditions such as high levels of detergent and DTT. As a proof of principle we applied TACh to frozen mouse liver tissue. Combined with massive parallel sequencing TACh identifies accessible regions that are associated with euchromatic features and accessibility at transcriptional start sites correlates positively with levels of gene transcription. Accessible chromatin identified by TACh overlaps to a large extend with accessible chromatin identified by DNase I using nuclei purified from freshly isolated liver tissue as starting material. The similarities are most pronounced at highly accessible regions, whereas identification of less accessible regions tends to be more divergence between nucleases. Interestingly, we show that some of the differences between DNase I and Benzonase relate to their intrinsic sequence biases and accordingly accessibility of CpG islands is probed more efficiently using TACh. Conclusion The TACh methodology identifies accessible chromatin derived from frozen tissue samples. We propose that this simple, robust approach can be applied

  15. A metabolite-centric view on flux distributions in genome-scale metabolic models.

    Science.gov (United States)

    Riemer, S Alexander; Rex, René; Schomburg, Dietmar

    2013-04-12

    Genome-scale metabolic models are important tools in systems biology. They permit the in-silico prediction of cellular phenotypes via mathematical optimisation procedures, most importantly flux balance analysis. Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models. We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions. The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens the door to systems biological

  16. Communications device identification methods, communications methods, wireless communications readers, wireless communications systems, and articles of manufacture

    Science.gov (United States)

    Steele, Kerry D [Kennewick, WA; Anderson, Gordon A [Benton City, WA; Gilbert, Ronald W [Morgan Hill, CA

    2011-02-01

    Communications device identification methods, communications methods, wireless communications readers, wireless communications systems, and articles of manufacture are described. In one aspect, a communications device identification method includes providing identification information regarding a group of wireless identification devices within a wireless communications range of a reader, using the provided identification information, selecting one of a plurality of different search procedures for identifying unidentified ones of the wireless identification devices within the wireless communications range, and identifying at least some of the unidentified ones of the wireless identification devices using the selected one of the search procedures.

  17. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

    DEFF Research Database (Denmark)

    Sanchez, Benjamin J.; Zhang, Xi-Cheng; Nilsson, Avlant

    2017-01-01

    Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics......, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance...... with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between...

  18. CONTROL SYSTEM IDENTIFICATION THROUGH MODEL MODULATION METHODS

    Science.gov (United States)

    identification has been achieved by using model modulation techniques to drive dynamic models into correspondence with operating control systems. The system ... identification then proceeded from examination of the model and the adaptive loop. The model modulation techniques applied to adaptive control

  19. Genome-scale Metabolic Reaction Modeling: a New Approach to Geomicrobial Kinetics

    Science.gov (United States)

    McKernan, S. E.; Shapiro, B.; Jin, Q.

    2014-12-01

    Geomicrobial rates, rates of microbial metabolism in natural environments, are a key parameter of theoretical and practical problems in geobiology and biogeochemistry. Both laboratory- and field-based approaches have been applied to study rates of geomicrobial processes. Laboratory-based approaches analyze geomicrobial kinetics by incubating environmental samples under controlled laboratory conditions. Field methods quantify geomicrobial rates by observing the progress of geomicrobial processes. To take advantage of recent development in biogeochemical modeling and genome-scale metabolic modeling, we suggest that geomicrobial rates can also be predicted by simulating metabolic reaction networks of microbes. To predict geomicrobial rates, we developed a genome-scale metabolic model that describes enzyme reaction networks of microbial metabolism, and simulated the network model by accounting for the kinetics and thermodynamics of enzyme reactions. The model is simulated numerically to solve cellular enzyme abundance and hence metabolic rates under the constraints of cellular physiology. The new modeling approach differs from flux balance analysis of system biology in that it accounts for the thermodynamics and kinetics of enzymatic reactions. It builds on subcellular metabolic reaction networks, and hence also differs from classical biogeochemical reaction modeling. We applied the new approach to Methanosarcina acetivorans, an anaerobic, marine methanogen capable of disproportionating acetate to carbon dioxide and methane. The input of the new model includes (1) enzyme reaction network of acetoclastic methanogenesis, and (2) representative geochemical conditions of freshwater sedimentary environments. The output of the simulation includes the proteomics, metabolomics, and energy and matter fluxes of M. acetivorans. Our simulation results demonstrate the predictive power of the new modeling approach. Specifically, the results illustrate how methanogenesis rates vary

  20. CFMDS: CUDA-based fast multidimensional scaling for genome-scale data.

    Science.gov (United States)

    Park, Sungin; Shin, Soo-Yong; Hwang, Kyu-Baek

    2012-01-01

    Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as much as possible. However, it is not trivial to apply it to genome-scale data (e.g., microarray gene expression profiles) on regular desktop computers, because of its high computational complexity. We implemented a highly-efficient software application, called CFMDS (CUDA-based Fast MultiDimensional Scaling), which produces an approximate solution of the classical MDS based on CUDA (compute unified device architecture) and the divide-and-conquer principle. CUDA is a parallel computing architecture exploiting the power of the GPU (graphics processing unit). The principle of divide-and-conquer was adopted for circumventing the small memory problem of usual graphics cards. Our application software has been tested on various benchmark datasets including microarrays and compared with the classical MDS algorithms implemented using C# and MATLAB. In our experiments, CFMDS was more than a hundred times faster for large data than such general solutions. Regarding the quality of dimensionality reduction, our approximate solutions were as good as those from the general solutions, as the Pearson's correlation coefficients between them were larger than 0.9. CFMDS is an expeditious solution for the data dimensionality reduction problem. It is especially useful for efficient processing of genome-scale data consisting of several thousands of objects in several minutes.

  1. Performance Test of System Identification Methods for a Nuclear Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Keuk Jong; Kim, Han Gon [KHNP, Daejeon (Korea, Republic of)

    2011-05-15

    An automatic controller that uses the model predictive control (MPC) method is being developed for automatic load follow operation. As described in Ref. a system identification method is important in the MPC method because MPC is based on a system model produced by system identification. There are many models and methods of system identification. In this study, AutoRegressive eXogenous (ARX) model was selected from among them, and the recursive least square (RLS) method and least square (LS) method associated with this model are used in a comparative performance analysis

  2. Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043

    Directory of Open Access Journals (Sweden)

    Oner Ebru

    2011-01-01

    Full Text Available Abstract Background Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043 is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype. Results C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress. Conclusions This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

  3. Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

    DEFF Research Database (Denmark)

    Olivares Hernandez, Roberto

    Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been......, translation initiation, translation elongation, translation termination, translation elongation, and mRNA decay. Considering these information from the mechanisms of transcription and translation, we will include this stoichiometric reactions into the genome scale model for S. Cerevisiae to obtain the first...

  4. Integrated genome-scale analysis of the transcriptional regulatory landscape in a blood stem/progenitor cell model.

    Science.gov (United States)

    Wilson, Nicola K; Schoenfelder, Stefan; Hannah, Rebecca; Sánchez Castillo, Manuel; Schütte, Judith; Ladopoulos, Vasileios; Mitchelmore, Joanna; Goode, Debbie K; Calero-Nieto, Fernando J; Moignard, Victoria; Wilkinson, Adam C; Jimenez-Madrid, Isabel; Kinston, Sarah; Spivakov, Mikhail; Fraser, Peter; Göttgens, Berthold

    2016-03-31

    Comprehensive study of transcriptional control processes will be required to enhance our understanding of both normal and malignant hematopoiesis. Modern sequencing technologies have revolutionized our ability to generate genome-scale expression and histone modification profiles, transcription factor (TF)-binding maps, and also comprehensive chromatin-looping information. Many of these technologies, however, require large numbers of cells, and therefore cannot be applied to rare hematopoietic stem/progenitor cell (HSPC) populations. The stem cell factor-dependent multipotent progenitor cell line HPC-7 represents a well-recognized cell line model for HSPCs. Here we report genome-wide maps for 17 TFs, 3 histone modifications, DNase I hypersensitive sites, and high-resolution promoter-enhancer interactomes in HPC-7 cells. Integrated analysis of these complementary data sets revealed TF occupancy patterns of genomic regions involved in promoter-anchored loops. Moreover, preferential associations between pairs of TFs bound at either ends of chromatin loops led to the identification of 4 previously unrecognized protein-protein interactions between key blood stem cell regulators. All HPC-7 data sets are freely available both through standard repositories and a user-friendly Web interface. Together with previously generated genome-wide data sets, this study integrates HPC-7 data into a genomic resource on par with ENCODE tier 1 cell lines and, importantly, is the only current model with comprehensive genome-scale data that is relevant to HSPC biology. © 2016 by The American Society of Hematology.

  5. Signal trend identification with fuzzy methods.

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.; Tsoukalas, L. H.; Wang, X.; Wei, T. Y. C.

    1999-08-19

    A fuzzy-logic-based methodology for on-line signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line signals and classify incoming power plant signals into increasing, decreasing and steady-state trend categories. In order to verify the methodology, a code named PROTREN is developed and tested using plant data. The results indicate that the code is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady-state signal as a transient one.

  6. Comparison of System Identification Methods using Ambient Bridge Test Data

    DEFF Research Database (Denmark)

    Andersen, P.; Brincker, Rune; Peeters, B.

    1999-01-01

    In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha......In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method...

  7. Comparison of System Identification Methods using Ambient Bridge Test Data

    DEFF Research Database (Denmark)

    Andersen, P.; Peeters, B.; Hermans, L.

    In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha......In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method...

  8. Propagator-based methods for recursive subspace model identification

    OpenAIRE

    Mercère, Guillaume; Bako, Laurent; Lecoeuche, Stéphane

    2008-01-01

    International audience; The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO output error state-space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorit...

  9. Review of Sequential Access Method for Fingerprint Identification

    Directory of Open Access Journals (Sweden)

    Saiful Akbar

    2012-06-01

    Full Text Available Real time fingerprint identification is usually equipped with specific computation machine architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint identification on common computation machine, a disquisition was conducted on sequential access method for fingerprint identification, with its underlying data structure designed to work without and with parallel processing. Hypothetically, parallel processing based on multi cores processor technology, can give faster result without reducing accuracy. Experiment confirms that speed performance of fingerprint identification using sequential access method with parallel processing outperforms the one without parallel processing. For both strategy, even though using parallel processing confirms faster result, experiment shows that searching time O(n still linearly depends on number of fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access method utilization.

  10. Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

    Directory of Open Access Journals (Sweden)

    Brooks J Paul

    2010-03-01

    Full Text Available Abstract Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405 is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum

  11. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

    Energy Technology Data Exchange (ETDEWEB)

    Navid, A; Almaas, E

    2009-01-13

    The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

  12. Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

    Science.gov (United States)

    Vivek-Ananth, R P; Samal, Areejit

    2016-09-01

    A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

  13. Genome-scale DNA sequence recognition by hybridization to short oligomers.

    Science.gov (United States)

    Milosavljević, A; Savković, S; Crkvenjakov, R; Salbego, D; Serrato, H; Kreuzer, H; Gemmell, A; Batus, S; Grujić, D; Carnahan, S; Tepavcević, J

    1996-01-01

    Recently developed hybridization technology (Drmanac et al. 1994) enables economical large-scale detection of short oligomers within DNA fragments. The newly developed recognition method (Milosavljević 1995b) enables comparison of lists of oligomers detected within DNA fragments against known DNA sequences. We here describe an experiment involving a set of 4,513 distinct genomic E.coli clones of average length 2kb, each hybridized with 636 randomly selected short oligomer probes. High hybridization signal with a particular probe was used as an indication of the presence of a complementary oligomer in the particular clone. For each clone, a list of oligomers with highest hybridization signals was compiled. The database consisting of 4,513 oligomer lists was then searched using known E.coli sequences as queries in an attempt to identify the clones that match the query sequence. Out of a total of 11 clones that were recognized at highest significance level by our method, 8 were single-pass sequenced from both ends. The single-pass sequenced ends were then compared against the query sequences. The sequence comparisons confirmed 7 out of the total of 8 examined recognitions. This experiment represents the first successful example of genome-scale sequence recognition based on hybridization data.

  14. Leveraged fault identification method for receiver autonomous integrity monitoring

    Directory of Open Access Journals (Sweden)

    Sun Yuan

    2015-08-01

    Full Text Available Receiver autonomous integrity monitoring (RAIM provides integrity monitoring of global positioning system (GPS for safety-of-life applications. In the process of RAIM, fault identification (FI enables navigation to continue in the presence of fault measurement. Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly. However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage. In this paper, the impact of leverage on the fault identification is analyzed. The leveraged RAIM fault identification (L-RAIM FI method is proposed with consideration of the difference in leverage for each satellite in view. Furthermore, the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method. The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.

  15. Comparison of System Identification Methods using Ambient Bridge Test Data

    DEFF Research Database (Denmark)

    Andersen, P.; Peeters, B.; Hermans, L.

    In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha......In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method......, the stochastic subspace method for estimation of state space systems and the prediction error method for estimation of Auto-Regressive Moving Average Vector models. It is not the entention to elect a winner among the four methods, but more to emphasize the different advantages of each of the methods....

  16. FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non-linear systems and obviously improve modeling accuracy.

  17. Efficient Parametric Identification Method for High Voltage Pulse Transformers

    CERN Document Server

    Aguglia, D; Viarouge, P; Cros, J

    2014-01-01

    This paper presents a new identification method for a pulse transformer equivalent circuit. It is based on an analytical approximation of the frequency-domain impedance data derived from a no-load test with open-circuited secondary winding and only requires measurements of primary current and voltage without phase data. Compared with time consuming and complex methods based on off-line non-linear identification procedures, this simple method also gives an estimation of the error on the identified parameters. The method is validated on an existing pulse transformer.

  18. Study on classification and identification methods of driver steering characteristics

    Directory of Open Access Journals (Sweden)

    Gang LI

    2015-12-01

    Full Text Available Aiming at the vehicle driver's steering characteristic classification and identification, the research method is initially explored based on CarSim simulation platform. The simulation experiment of steering condition is designed and the test data is collected. According to the maximum yaw rate of the vehicle, the driver steering characteristics are classified by K-means clustering algorithm. The driver steering characteristics identification models are established by learning vector quantization (LVQ neural network, BP neural network, and support vector machine (SVM respectively in the environment of Matlab software. The test experiment and comparison are done for the three kinds of approaches, and the results show that all those three kinds of identification approaches have high accuracy, and the SVM method has a certain advantage on driver steering characteristics identification.

  19. AtPID: a genome-scale resource for genotype–phenotype associations in Arabidopsis

    Science.gov (United States)

    Lv, Qi; Lan, Yiheng; Shi, Yan; Wang, Huan; Pan, Xia; Li, Peng; Shi, Tieliu

    2017-01-01

    AtPID (Arabidopsis thaliana Protein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way. PMID:27899679

  20. MapMaker and PathTracer for tracking carbon in genome-scale metabolic models.

    Science.gov (United States)

    Tervo, Christopher J; Reed, Jennifer L

    2016-05-01

    Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites.

  1. Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints.

    Science.gov (United States)

    Yen, Jiun Y; Nazem-Bokaee, Hadi; Freedman, Benjamin G; Athamneh, Ahmad I M; Senger, Ryan S

    2013-05-01

    Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H2 production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design "fine-tuned" metabolic engineering strategies in silico that can be implemented directly with available genomic tools.

  2. Analysis of genetic variation and potential applications in genome-scale metabolic modeling

    Directory of Open Access Journals (Sweden)

    João Gonçalo Rocha Cardoso

    2015-02-01

    Full Text Available Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

  3. The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum.

    Science.gov (United States)

    Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

    2013-01-01

    We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.

  4. The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum.

    Directory of Open Access Journals (Sweden)

    Rasmus Agren

    Full Text Available We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.

  5. Identification and Definition of Early Aspects - A Prototype of Method

    OpenAIRE

    Resende, Antônio Maria Pereira de; Federal University of Lavras; da Cunha, Adilson Marques; Technological Institute of Aeronautics; Costa, Heitor Augustus Xavier; Federal University of Lavras

    2010-01-01

    This paper briefly presents the conceptual model of the Method for Early Aspect (EA) Identification and Definition (MEAID). It also details activities named Early Aspects Candidates Identification with a set of heuristics and Early Aspects Definition with decision equation. The MEAID has being developed to support software engineering professionals reduce empirical and subjective decisions, aiming to increase efficacy and efficiency on such activity. Results from a scientific experimentation,...

  6. Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology.

    Science.gov (United States)

    Milne, Caroline B; Kim, Pan-Jun; Eddy, James A; Price, Nathan D

    2009-12-01

    Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

  7. Systems biology as a foundation for genome-scale synthetic biology.

    Science.gov (United States)

    Barrett, Christian L; Kim, Tae Yong; Kim, Hyun Uk; Palsson, Bernhard Ø; Lee, Sang Yup

    2006-10-01

    As the ambitions of synthetic biology approach genome-scale engineering, comprehensive characterization of cellular systems is required, as well as a means to accurately model cell-scale molecular interactions. These requirements are coincident with the goals of systems biology and, thus, systems biology will become the foundation for genome-scale synthetic biology. Systems biology will form this foundation through its efforts to reconstruct and integrate cellular systems, develop the mathematics, theory and software tools for the accurate modeling of these integrated systems, and through evolutionary mechanisms. As genome-scale synthetic biology is so enabled, it will prove to be a positive feedback driver of systems biology by exposing and forcing researchers to confront those aspects of systems biology which are inadequately understood.

  8. New inverse method for identification of constitutive parameters

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A new inverse method by coupling iSIGHT and ABAQUS was proposed to determine the constitutive parameters of Al2O3sf/LY12 composite deforming at elevated temperature. It combined the merits of the finite element simulation and optimization technique. The direct model was simulated with finite element code ABAQUS. The inverse problem associated with the identification of the constitutive parameters was expressed as a least square optimization problem. The direct simulation and the parameters optimization were implemented in iSIGHT integrated environment. The aim was to match the output of the direct simulation with the experiment data.The capability of the proposed inverse method was demonstrated through the identification of constitutive parameters of Al2O3sf/LY12composite. The proposed new inverse method is also applicable to other parameters identification which is hardly determined through experiments or direct analytical method.

  9. Nonlinear system identification with global and local soft computing methods

    Energy Technology Data Exchange (ETDEWEB)

    Runkler, T.A. [Siemens AG, Muenchen (Germany). Zentralabt. Technik Information und Kommunikation

    2000-10-01

    An important step in the design of control systems is system identification. Data driven system identification finds functional models for the system's input output behavior. Regression methods are simple and effective, but may cause overshoots for complicated characteristics. Neural network approaches such as the multilayer perceptron yield very accurate models, but are black box approaches which leads to problems in system and stability analysis. In contrast to these global modeling methods crisp and fuzzy rule bases represent local models that can be extracted from data by clustering methods. Depending on the type and number of models different degrees of model accuracy can be achieved. (orig.)

  10. New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

    Science.gov (United States)

    Gamermann, Daniel; Montagud, Arnaud; Conejero, J Alberto; Urchueguía, Javier F; de Córdoba, Pedro Fernández

    2014-07-01

    A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

  11. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

    Science.gov (United States)

    Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

    2015-01-01

    Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

  12. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

    Directory of Open Access Journals (Sweden)

    Katsunori Yoshikawa

    Full Text Available Arthrospira (Spirulina platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(PH dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

  13. Identification of Molecular Laser Transitions Using the Finite Element Method

    Science.gov (United States)

    1995-12-01

    Vibration Levels," Physical Review, Vol. 34, 1929 12 Arfken , George Mathematical Methods for Physicists. San Diego: Academic Press, Inc. 1985 13...unsolvable. Mathematical techniques such as the classical Rayleigh-Ritz method , variational calculus, and Galerkin’s weighted residuals method , much...AFIT/GAP/ENP/95D-14 IDENTIFICATION OF MOLECULAR LASER TRANSITIONS USING THE FINITE ELEMENT METHOD THESIS Matthew C. Smitham, Captain, USAF AFIT/GAP

  14. Methodes for identification of specific language impairment

    Directory of Open Access Journals (Sweden)

    Toktam Maleki Shahmahmood

    2014-06-01

    Full Text Available Background and Aim: Specific language impiarment (SLI is one of the most prevalent developmental language disorders its diagnosis is a problematic issue among researchers and clinicians because of the heterogeneity of language profiles in the affected population and overlapping with other developmental language disorders. The aim of this study was to review the suggested diagnostic criteria for this disorder, controversies about these criteria and identify the most accurate diagnostic methods.Methods: Published article from 1980 to 2012 in bibliographic and publisher databases including Pubmed, Google scholar, Cochran library, Web of Science, ProQuest, Springer, Oxford, Science direct, Ovid, Iran Medex and Magiran about the diagnostic methods for discriminating preschoool children with specific language impiarment from normal developing children were reviewd in this article. These keywords were used for research: “specific language impairment”, “SLI”, “diagnosis or identification”, “standardized tests”, and “tests for language development”.Conclusion: The results of this study show inspite of agreement of researchers and clinicians about exclusionary criteria as one basic part of the diagnosis of specific language impiarment , there is no consensus about the other part, inclusionary criteria. Different studies used different inclusionary criteria which can be divided to categories of clincal judgment, discrepancy-based criteria, standardized testing, clinical markers and markers from spontaneous speech samples. Advantages, disadvantages, and clinical applicability of each diagnostic method are discussed in this article.

  15. Genome-scale engineering for systems and synthetic biology

    OpenAIRE

    Esvelt, Kevin Michael; Wang, Harris H.

    2013-01-01

    Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review ...

  16. Yeast identification in the clinical microbiology laboratory: phenotypical methods.

    Science.gov (United States)

    Freydiere, A M; Guinet, R; Boiron, P

    2001-02-01

    Emerging yeast pathogens are favoured by increasing numbers of immunocompromised patients and by certain current medical practices. These yeasts differ in their antifungal drug susceptibilities, and rapid species identification is imperative. A large variety of methods have been developed with the aim of facilitating rapid, accurate yeast identification. Significant recent commercial introductions have included species-specific direct enzymatic colour tests, differential chromogenic isolation plates, direct immunological tests, and enhanced manual and automated biochemical and enzymatic panels. Chromogenic isolation media demonstrate better detection rates of yeasts in mixed cultures than traditional media, and allow the direct identification of Candida albicans by means of colony colour. Comparative evaluation of rapid methods for C. albicans identification, including the germ tube test, shows that chromogenic media may be economically advantageous. Accurate tests for single species include the Bichrolatex Albicans and Krusei Color tests, both immunologically based, as well as the Remel Rapid Trehalose Assimilation Broth for C. glabrata. Among broad-spectrum tests, the RapID Yeast Plus system gives same-day identification of clinical yeasts, but performance depends on inoculum density and geographic isolate source. The API 20 C AUX system is considered a reference method, but newer systems such as Auxacolor and Fungichrom are as accurate and are more convenient. Among automated systems, the ID 32 C strip, the Vitek Yeast Biochemical Card and the Vitek 2 ID-YST system correctly identify >93% of common yeasts, but the ID-YST is the most accurate with uncommon yeasts, including C. dubliniensis. Spectroscopic methods such as Fourier transformed-infrared spectroscopy offer potential advantages for the future. Overall, the advantages of rapid yeast identification methods include relative simplicity and low cost. For all rapid methods, meticulous, standardized

  17. DNA methods for identification of Chinese medicinal materials

    Directory of Open Access Journals (Sweden)

    Yip Pui

    2007-09-01

    Full Text Available Abstract As adulterated and substituted Chinese medicinal materials are common in the market, therapeutic effectiveness of such materials cannot be guaranteed. Identification at species-, strain- and locality-levels, therefore, is required for quality assurance/control of Chinese medicine. This review provides an informative introduction to DNA methods for authentication of Chinese medicinal materials. Technical features and examples of the methods based on sequencing, hybridization and polymerase chain reaction (PCR are described and their suitability for different identification objectives is discussed.

  18. Generating Genome-Scale Candidate Gene Lists for Pharmacogenomics

    DEFF Research Database (Denmark)

    Hansen, Niclas Tue; Brunak, Søren; Altman, R. B.

    2009-01-01

    A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge...

  19. Impact of identity theft on methods of identification.

    Science.gov (United States)

    McLemore, Jerri; Hodges, Walker; Wyman, Amy

    2011-06-01

    Responsibility for confirming a decedent's identity commonly falls on the shoulders of the coroner or medical examiner. Misidentification of bodies results in emotional turmoil for the next-of-kin and can negatively impact the coroner's or medical examiner's career. To avoid such mishaps, the use of scientific methods to establish a positive identification is advocated. The use of scientific methods of identification may not be reliable in cases where the decedent had assumed the identity of another person. Case studies of erroneously identified bodies due to identity theft from the state medical examiner offices in Iowa and New Mexico are presented. This article discusses the scope and major concepts of identity theft and how identity theft prevents the guarantee of a positive identification.

  20. Methods of Identification and Evaluation of Brownfield Sites

    Directory of Open Access Journals (Sweden)

    Safet Kurtovic

    2016-01-01

    Full Text Available The basic objective of this paper was to determine the importance and potential restoration of brownfield sites in terms of economic prosperity of a particular region or country. In addition, in a theoretical sense, this paper presents the methods used in the identification of brownfield sites such as Smart Growth Network model and Thomas GIS model, and methods for evaluation of brownfield sites or the indexing method, cost-benefit and multivariate analysis.

  1. Methods of Identification and Evaluation of Brownfield Sites

    Directory of Open Access Journals (Sweden)

    Safet Kurtović

    2014-04-01

    Full Text Available The basic objective of this paper was to determine the importance and potential restoration of brownfield sites in terms of economic prosperity of a particular region or country. In addition, in a theoretical sense, this paper presents the methods used in the identification of brownfield sites such as Smart Growth Network model and Thomas GIS model, and methods for evaluation of brownfield sites or the indexing method, cost-benefit and multivariate analysis.

  2. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    DEFF Research Database (Denmark)

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized...

  3. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

    DEFF Research Database (Denmark)

    Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.

    2017-01-01

    Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many...

  4. Interplay between Constraints, Objectives, and Optimality for Genome-Scale Stoichiometric Models

    NARCIS (Netherlands)

    Maarleveld, T.R.; Wortel, M.; Olivier, B.G.; Teusink, B.; Bruggeman, F.J.

    2015-01-01

    High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important ins

  5. MultiMetEval : Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

    NARCIS (Netherlands)

    Zakrzewski, Piotr; Medema, Marnix H.; Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko; Fong, Stephen S.

    2012-01-01

    Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the co

  6. Challenges in experimental data integration within genome-scale metabolic models

    Directory of Open Access Journals (Sweden)

    Képès François

    2010-04-01

    Full Text Available Abstract A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincaré, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.

  7. The architecture of ArgR-DNA complexes at the genome-scale in> Escherichia coli

    DEFF Research Database (Denmark)

    Cho, Suhyung; Cho, Yoo-Bok; Kang, Taek Jin;

    2015-01-01

    DNA-binding motifs that are recognized by transcription factors (TFs) have been well studied; however, challenges remain in determining the in vivo architecture of TF-DNA complexes on a genome-scale. Here, we determined the in vivo architecture of Escherichia coli arginine repressor (ArgR)-DNA co...

  8. Comparative genome-scale metabolic modeling of actinomycetes : The topology of essential core metabolism

    NARCIS (Netherlands)

    Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of act

  9. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism.

    NARCIS (Netherlands)

    Alam, M.T.; Medema, M.H.; Takano, E.; Breitling, R.

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of act

  10. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism.

    NARCIS (Netherlands)

    Alam, M.T.; Medema, M.H.; Takano, E.; Breitling, R.

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of

  11. Comparative genome-scale metabolic modeling of actinomycetes : The topology of essential core metabolism

    NARCIS (Netherlands)

    Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of

  12. Genome-scale reconstruction and analysis of the metabolic network in the hyperthermophilic archaeon Sulfolobus solfataricus.

    Directory of Open Access Journals (Sweden)

    Thomas Ulas

    Full Text Available We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2-4 (optimum 3.5 and a temperature of 75-80°C (optimum 80°C. The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose. Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA, which predicted that 18% of all possible single gene deletions would be lethal for the organism.

  13. System identification methods for aircraft flight control development and validation

    Science.gov (United States)

    Tischler, Mark B.

    1995-01-01

    System-identification methods compose a mathematical model, or series of models, from measurements of inputs and outputs of dynamic systems. The extracted models allow the characterization of the response of the overall aircraft or component subsystem behavior, such as actuators and on-board signal processing algorithms. This paper discusses the use of frequency-domain system-identification methods for the development and integration of aircraft flight-control systems. The extraction and analysis of models of varying complexity from nonparametric frequency-responses to transfer-functions and high-order state-space representations is illustrated using the Comprehensive Identification from FrEquency Responses (CIFER) system-identification facility. Results are presented for test data of numerous flight and simulation programs at the Ames Research Center including rotorcraft, fixed-wing aircraft, advanced short takeoff and vertical landing (ASTOVL), vertical/short takeoff and landing (V/STOL), tiltrotor aircraft, and rotor experiments in the wind tunnel. Excellent system characterization and dynamic response prediction is achieved for this wide class of systems. Examples illustrate the role of system-identification technology in providing an integrated flow of dynamic response data around the entire life-cycle of aircraft development from initial specifications, through simulation and bench testing, and into flight-test optimization.

  14. Comment on "Method of Fingerprint Identification"

    Directory of Open Access Journals (Sweden)

    Ford L. Gaol

    2011-01-01

    Full Text Available Problem statement: Paper that written by Surachai Panich reported matching two fingerprints taken from small database of fingerprints. Approach: In this study we used extensive literature review and research publication. We also compare with some best practices that applied in the business and industrial field for matching the fingerprints. Results: Surachai Panich used his method only for around one hundred data. However, we found that in the recent research most of the fingerprint problems are explore in million of data. I found some of the literature review are quiet old. Since the advancement of Fingerprint matching is increase very rapidly so the comparison with the recent publication is a must. The comparison has to be in the experimental using million of data as the input. We do not find such experimental result on this paper. Conclusion/Recommendations: The paper should explore more in biometrics feature extraction for matching process. The biometrics have analogy to the use of physiological or biological characteristics to measure the identity of an individual. These features are assuming unique to each individual and remain unaltered during a person’s lifetime. These features make biometrics a promising solution to the society.

  15. Identification Method in Software Process Improvement Areas

    Science.gov (United States)

    Hayashi, Akihiro; Kataoka, Nobuhiro

    With the prevalence of CMMI (Capability Maturity Model Integration) that is developed in the United States and the international standardization model of Software Process Assessment ISO/IEC 15504, SPI (Software Process Improvement) based on Software Process Assessment is gaining ground in Japan as well. One of the objectives in SPI is to prevent the occurrence of errors in the downstream process by thorough process management in the upstream process. In order to implement the SPI from such viewpoints, this paper suggests the method to analyze Problem Reports developed in the testing process of a project, to specify software bugs existent in leading processes and to identify the SPI in the upstream process. By means of Problem Reports that is capable of externalization and is used in testing processes, knowledge that has not been externalized will be specified as Defect Cluster. Furthermore, deliverables in each process will be identified from the properties of Defect Cluster to implement the SPI, which is of the essence, by identifying processes causing trouble. In the project that was the analytical target, there were 608 Problem Reports in its testing process. As a result of the analysis, it was found that by identifying processes in the upper process to improve with the techniques recommended in this paper, about 13% of cases causing trouble could be prevented from occurring, which will contribute to productivity improvement due to a decrease in the number of rework processes.

  16. LocateP: Genome-scale subcellular-location predictor for bacterial proteins

    Directory of Open Access Journals (Sweden)

    Zhou Miaomiao

    2008-03-01

    Full Text Available Abstract Background In the past decades, various protein subcellular-location (SCL predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. Results LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms

  17. A comparison of clustering methods for writer identification and verification

    NARCIS (Netherlands)

    Bulacu, M.L.; Schomaker, L.R.B.

    2005-01-01

    An effective method for writer identification and verification is based on assuming that each writer acts as a stochastic generator of ink-trace fragments, or graphemes. The probability distribution of these simple shapes in a given handwriting sample is characteristic for the writer and is computed

  18. Methods and Approaches to Mass Spectroscopy Based Protein Identification

    Science.gov (United States)

    This book chapter is a review of current mass spectrometers and the role in the field of proteomics. Various instruments are discussed and their strengths and weaknesses are highlighted. In addition, the methods of protein identification using a mass spectrometer are explained as well as data vali...

  19. Study on Correlation and Quantitative Error Estimation Method Among the Splitting Shear Wave Identification Methods

    Institute of Scientific and Technical Information of China (English)

    Liu Xiqiang; Zhou Huilan; Li Hong; Gai Dianguang

    2000-01-01

    Based on the propagation characteristics of shear wave in the anisotropic layers, thecorrelation among several splitting shear-wave identification methods hasbeen studied. Thispaper puts forward the method estimating splitting shear-wave phases and its reliability byusing of the assumption that variance of noise and useful signal data obey normaldistribution. To check the validity of new method, the identification results and errorestimation corresponding to 95% confidence level by analyzing simulation signals have beengiven.

  20. Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture

    Science.gov (United States)

    Lassahn, Gordon D.; Lancaster, Gregory D.; Apel, William A.; Thompson, Vicki S.

    2013-01-08

    Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture are described. According to one embodiment, an image portion identification method includes accessing data regarding an image depicting a plurality of biological substrates corresponding to at least one biological sample and indicating presence of at least one biological indicator within the biological sample and, using processing circuitry, automatically identifying a portion of the image depicting one of the biological substrates but not others of the biological substrates.

  1. Probability of identification (POI): a statistical model for the validation of qualitative botanical identification methods

    Science.gov (United States)

    A qualitative botanical identification method (BIM) is an analytical procedure which returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) mate...

  2. Genome-scale co-evolutionary inference identifies functions and clients of bacterial Hsp90.

    Directory of Open Access Journals (Sweden)

    Maximilian O Press

    Full Text Available The molecular chaperone Hsp90 is essential in eukaryotes, in which it facilitates the folding of developmental regulators and signal transduction proteins known as Hsp90 clients. In contrast, Hsp90 is not essential in bacteria, and a broad characterization of its molecular and organismal function is lacking. To enable such characterization, we used a genome-scale phylogenetic analysis to identify genes that co-evolve with bacterial Hsp90. We find that genes whose gain and loss were coordinated with Hsp90 throughout bacterial evolution tended to function in flagellar assembly, chemotaxis, and bacterial secretion, suggesting that Hsp90 may aid assembly of protein complexes. To add to the limited set of known bacterial Hsp90 clients, we further developed a statistical method to predict putative clients. We validated our predictions by demonstrating that the flagellar protein FliN and the chemotaxis kinase CheA behaved as Hsp90 clients in Escherichia coli, confirming the predicted role of Hsp90 in chemotaxis and flagellar assembly. Furthermore, normal Hsp90 function is important for wild-type motility and/or chemotaxis in E. coli. This novel function of bacterial Hsp90 agreed with our subsequent finding that Hsp90 is associated with a preference for multiple habitats and may therefore face a complex selection regime. Taken together, our results reveal previously unknown functions of bacterial Hsp90 and open avenues for future experimental exploration by implicating Hsp90 in the assembly of membrane protein complexes and adaptation to novel environments.

  3. Factors affecting reproducibility between genome-scale siRNA-based screens

    Science.gov (United States)

    Barrows, Nicholas J.; Le Sommer, Caroline; Garcia-Blanco, Mariano A.; Pearson, James L.

    2011-01-01

    RNA interference-based screening is a powerful new genomic technology which addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, we performed two identically designed small interfering RNA (siRNA)-based, whole genome screens for host factors supporting yellow fever virus infection. These screens represent two separate experiments completed five months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole genome screens. Application of these analysis methodologies within a single screening dataset using a fixed threshold equivalent to a p-value ≤ 0.001 resulted in hit lists ranging from 82 to 1,140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and inter-screen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least two independent siRNAs for each gene product in primary screens. To facilitate validation we conclude by suggesting methods to reduce false discovery at the primary screening stage. In this study we present the first comprehensive comparison of multiple analysis strategies, and demonstrate the impact of the analysis methodology on the composition of the “hit list”. Therefore, we propose that the entire dataset derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies. PMID:20625183

  4. A modified star map identification method suitable for astronomical camera

    Science.gov (United States)

    Liu, Meiying; Wang, Hu; Wen, Desheng; Liu, Jie; Xue, Yaoke; Liu, Yang; Zhao, Hui

    2015-10-01

    High accuracy star map identification results are the basis of astronomical positioning. The traditional triangle star identification algorithm has a higher redundancy and a poor robustness to noise. Considering the specific requirements of the star map identification of the astronomical camera, in allusion to this default, proceeding with selection of guide stars, construction of guide star catalogue and realization of matching algorithm, a modified triangle algorithm based on traditional one is presented. With the proposed algorithm, the guide star is selected from astronomical durchmusterung. In order to speed up guide star indexing, the guide star catalogue is founded after dividing the sky using the overlapping rectangle method. The guide star sub-catalogue is constructed by the radius of guide triangle circumcircle and the two sides of guide triangle. The characteristic radius is used for indexing and sorted in an ascending order to improve the searching efficiency in the processing of star map identification. The matching scope of the angular distance is narrowed and the matching rate of angular distance is improved by the matching of the characteristics radius. If there exists redundancy, a normalized magnitude is used to eliminate it. Within the observing area of the real sky, the 1050 star maps continuously are calculated. The simulation results show that, the identification rate of this algorithm is greater than 97. 83% when the noise of position error is two pixels, and the average identification time is about 25. 07ms. Compared with the traditional triangle algorithm, this modified algorithm has a couple of advantages, including the smaller storage capacity of guide star catalogue, better robustness to position and magnitude error, higher rate of correcting star map identification and lower redundancy.

  5. Symbolic flux analysis for genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Peterson Pearu

    2011-05-01

    Full Text Available Abstract Background With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks. Results A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast. Conclusions We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.

  6. Symbolic flux analysis for genome-scale metabolic networks.

    Science.gov (United States)

    Schryer, David W; Vendelin, Marko; Peterson, Pearu

    2011-05-23

    With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks. A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast. We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.

  7. Spoken Language Identification Using Hybrid Feature Extraction Methods

    CERN Document Server

    Kumar, Pawan; Mishra, A N; Chandra, Mahesh

    2010-01-01

    This paper introduces and motivates the use of hybrid robust feature extraction technique for spoken language identification (LID) system. The speech recognizers use a parametric form of a signal to get the most important distinguishable features of speech signal for recognition task. In this paper Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) along with two hybrid features are used for language Identification. Two hybrid features, Bark Frequency Cepstral Coefficients (BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) were obtained from combination of MFCC and PLP. Two different classifiers, Vector Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM) were used for classification. The experiment shows better identification rate using hybrid feature extraction techniques compared to conventional feature extraction methods.BFCC has shown better performance than MFCC with both classifiers. RPLP along with GMM has shown be...

  8. Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

    Science.gov (United States)

    Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Boussau, Bastien; Warnow, Tandy

    2015-01-01

    Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically

  9. Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses.

    Directory of Open Access Journals (Sweden)

    Md Shamsuzzoha Bayzid

    Full Text Available Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS, modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees, they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014 presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also

  10. Parameter Identification Method for SINS Initial Alignment under Inertial Frame

    Directory of Open Access Journals (Sweden)

    Haijian Xue

    2016-01-01

    Full Text Available The performance of a strapdown inertial navigation system (SINS largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-position method; then the time of initial alignment is greatly prolonged. For this issue, a novel self-alignment algorithm by parameter identification method under inertial frame for SINS is proposed in this paper. Firstly, this coarse alignment method using the gravity in the inertial frame as a reference is discussed to overcome the limit of dynamic disturbance on a rocking base and fulfill the requirement for the fine alignment. Secondly, the fine alignment method by parameter identification under inertial frame is formulated. The theoretical analysis results show that the fine alignment model is fully self-aligned with no external reference information and the gyrodrifts can be estimated in real time. The simulation results demonstrate that the proposed method can achieve rapid and highly accurate initial alignment for SINS.

  11. Method for experimental determination of flutter speed by parameter identification

    Science.gov (United States)

    Nissim, E.; Gilyard, Glenn B.

    1989-01-01

    A method for flight flutter testing is proposed which enables one to determine the flutter dynamic pressure from flights flown far below the flutter dynamic pressure. The method is based on the identification of the coefficients of the equations of motion at low dynamic pressures, followed by the solution of these equations to compute the flutter dynamic pressure. The initial results of simulated data reported in the present work indicate that the method can accurately predict the flutter dynamic pressure, as described. If no insurmountable difficulties arise in the implementation of this method, it may significantly improve the procedures for flight flutter testing.

  12. Network Traffic Anomalies Identification Based on Classification Methods

    Directory of Open Access Journals (Sweden)

    Donatas Račys

    2015-07-01

    Full Text Available A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies.

  13. Optimum Identification Method of Sorting Green Household Waste

    Directory of Open Access Journals (Sweden)

    Daud Mohd Hisam

    2016-01-01

    Full Text Available This project is related to design of sorting facility for reducing, reusing, recycling green waste material, and in particular to invent an automatic system to distinguish household waste in order to separate them from the main waste stream. The project focuses on thorough analysis of the properties of green household waste. The method of identification is using capacitive sensor where the characteristic data taken on three different sensor drive frequency. Three types of material have been chosen as a medium of this research, to be separated using the selected method. Based on capacitance characteristics and its ability to penetrate green object, optimum identification method is expected to be recognized in this project. The output capacitance sensor is in analogue value. The results demonstrate that the information from the sensor is enough to recognize the materials that have been selected.

  14. Identification of individuals using palatal rugae: Computerized method

    Directory of Open Access Journals (Sweden)

    M Hemanth

    2010-01-01

    Full Text Available Identification of individuals is a challenging task in forensic odontology. In circumstances where identification of an individual by fingerprint or dental record comparison is difficult, the palatal rugae may be considered as an alternative source. Palatal rugae have been shown to be highly individualistic and it maintains consistency in shape throughout life. Aims and Objectives: The present study is conducted to test the efficiency of computerized software in the identification of individuals after obtaining digital photographic images of the rugae. Materials and Methods: The intra oral photographs of 100 individuals were taken using a SLR digital camera. The custom made external attachment was attached to the camera to standardize all the photographs. A special software was designed called the Palatal Rugae Comparison Software (PRCS Version 2.0 to match the clinical photographs. Five evaluators including 3 dentists, 1 computer professional, and 1 general surgeon were asked to match the rugae pattern using the software. The results were recorded along with time taken by each operator to match all the photos using software. Results: The software recorded an accuracy of 99% in identification of individuals. Conclusion: The present study supports the fact of individuality of the rugae. Computerized method has given very good results to support the individualization of rugae. Through our study, we feel that palatal rugae patterns will be of great use in the future of forensic odontology.

  15. Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling

    DEFF Research Database (Denmark)

    McGarrity, Sarah; Halldórsson, Haraldur; Palsson, Sirus

    2016-01-01

    of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford...... mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus...... on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health...

  16. Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis

    Science.gov (United States)

    Simeone, Ines; Anjum, Samreen; Mokrab, Younes; Bertucci, François; Finetti, Pascal; Curigliano, Giuseppe; Cerulo, Luigi; Tomei, Sara; Delogu, Lucia Gemma; Maccalli, Cristina; Miller, Lance D.; Ceccarelli, Michele

    2017-01-01

    ABSTRACT Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as PDL1, PD1, FOXP3, IDO1, and CTLA4, was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of TP53 were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of MAP3K1 and MAP2K4 mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity. PMID:28344865

  17. System Identification and POD Method Applied to Unsteady Aerodynamics

    Science.gov (United States)

    Tang, Deman; Kholodar, Denis; Juang, Jer-Nan; Dowell, Earl H.

    2001-01-01

    The representation of unsteady aerodynamic flow fields in terms of global aerodynamic modes has proven to be a useful method for reducing the size of the aerodynamic model over those representations that use local variables at discrete grid points in the flow field. Eigenmodes and Proper Orthogonal Decomposition (POD) modes have been used for this purpose with good effect. This suggests that system identification models may also be used to represent the aerodynamic flow field. Implicit in the use of a systems identification technique is the notion that a relative small state space model can be useful in describing a dynamical system. The POD model is first used to show that indeed a reduced order model can be obtained from a much larger numerical aerodynamical model (the vortex lattice method is used for illustrative purposes) and the results from the POD and the system identification methods are then compared. For the example considered, the two methods are shown to give comparable results in terms of accuracy and reduced model size. The advantages and limitations of each approach are briefly discussed. Both appear promising and complementary in their characteristics.

  18. WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences

    Science.gov (United States)

    Metwally, Ahmed A.; Dai, Yang; Finn, Patricia W.; Perkins, David L.

    2016-01-01

    Background Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time. Methods In this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity. Conclusions WEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE. PMID:27683082

  19. A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples.

    Science.gov (United States)

    Song, Young Kyoung; Hong, Sang Hee; Jang, Mi; Han, Gi Myung; Rani, Manviri; Lee, Jongmyoung; Shim, Won Joon

    2015-04-15

    The analysis of microplastics in various environmental samples requires the identification of microplastics from natural materials. The identification technique lacks a standardized protocol. Herein, stereomicroscope and Fourier transform infrared spectroscope (FT-IR) identification methods for microplastics (microplastics were significantly (p0.05) different. Depending on the number of samples and the microplastic size range of interest, the appropriate identification method should be determined; selecting a suitable identification method for microplastics is crucial for evaluating microplastic pollution.

  20. Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states

    DEFF Research Database (Denmark)

    Cho, Byung-Kwan; Kim, Donghyuk; Knight, Eric M.

    2014-01-01

    to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative sigma-factors (the sigma(70) and sigma(38) regulons), confirming the competition model of sigma substitution......Background: At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a sigma-factor to recognize the genomic location at which the process initiates. Although the crucial role of sigma-factors has long been appreciated and characterized for many individual...... promoters, we do not yet have a genome-scale assessment of their function. Results: Using multiple genome-scale measurements, we elucidated the network of s-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 sigma-factor-specific promoters corresponding...

  1. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins

    DEFF Research Database (Denmark)

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J.; Shojaosadati, Seyed Abbas;

    2016-01-01

    Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins...... produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address...... native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better...

  2. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Palsson, Bernhard; Feist, Adam

    2013-01-01

    The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction...... of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective...... on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges...

  3. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Directory of Open Access Journals (Sweden)

    Julián Triana

    2014-08-01

    Full Text Available The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  4. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Science.gov (United States)

    Triana, Julián; Montagud†, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F.

    2014-01-01

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942. PMID:25141288

  5. Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

    Science.gov (United States)

    LaBudde, Robert A; Harnly, James M

    2012-01-01

    A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

  6. Reproducing wavelet kernel method in nonlinear system identification

    Institute of Scientific and Technical Information of China (English)

    WEN Xiang-jun; XU Xiao-ming; CAI Yun-ze

    2008-01-01

    By combining the wavelet decomposition with kernel method, a practical approach of universal multi-scale wavelet kernels constructed in reproducing kernel Hilbert space (RKHS) is discussed, and an identifica-tion scheme using wavelet support vector machines ( WSVM ) estimator is proposed for nonlinear dynamic sys-tems. The good approximating properties of wavelet kernel function enhance the generalization ability of the pro-posed method, and the comparison of some numerical experimental results between the novel approach and some existing methods is encouraging.

  7. EMD Method Applied to Identification of Logging Sequence Strata

    Directory of Open Access Journals (Sweden)

    Zhao Ni

    2015-10-01

    Full Text Available In this work, we compare Fourier transform, wavelet transform, and empirical mode decomposition (EMD, and point out that EMD method decomposes complex signal into a series of component functions through curves of local mean value. Each of Intrinsic Mode Functions (IMFs - component functions contains all the information on the original signal. Therefore, it is more suitable for the interface identification of logging sequence strata.

  8. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

    Science.gov (United States)

    Papin, Jason A; Price, Nathan D; Edwards, Jeremy S; Palsson B, Bernhard Ø

    2002-03-07

    Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

  9. Online System Identification Method Using Modified Regularized Exponential Forgetting

    Directory of Open Access Journals (Sweden)

    Ján VACHÁLEK

    2013-12-01

    Full Text Available The paper deals with the use of regularized exponential forgetting (REF in the process of online system identification. The deployment of this type of forgetting strategy is advantageous for very long runs with small changes in the identified input parameters (in the range of 100 000 steps. In these cases, the classical methods of forgetting, such as an exponential (EF or directional forgetting (DF lack the required quality and reach the limit of numerical stability of the calculations of system parameters, which may lead to the early termination of system identification procedure. To avoid this undesirable effect and maintain sufficient primary information about the identified system, a modified REF method is used that employs alternative covariance matrix (ACM formulation to store the primary information of the identified system (REFACM and prevents the numerical destabilization of the identification process. The quality of the modified REFACM forgetting method —along with its validation and comparison with REZ to verify its properties—is performed using standard tests.

  10. Construction of an E. Coli genome-scale atom mapping model for MFA calculations.

    Science.gov (United States)

    Ravikirthi, Prabhasa; Suthers, Patrick F; Maranas, Costas D

    2011-06-01

    Metabolic flux analysis (MFA) has so far been restricted to lumped networks lacking many important pathways, partly due to the difficulty in automatically generating isotope mapping matrices for genome-scale metabolic networks. Here we introduce a procedure that uses a compound matching algorithm based on the graph theoretical concept of pattern recognition along with relevant reaction information to automatically generate genome-scale atom mappings which trace the path of atoms from reactants to products for every reaction. The procedure is applied to the iAF1260 metabolic reconstruction of Escherichia coli yielding the genome-scale isotope mapping model imPR90068. This model maps 90,068 non-hydrogen atoms that span all 2,077 reactions present in iAF1260 (previous largest mapping model included 238 reactions). The expanded scope of the isotope mapping model allows the complete tracking of labeled atoms through pathways such as cofactor and prosthetic group biosynthesis and histidine metabolism. An EMU representation of imPR90068 is also constructed and made available.

  11. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

    Science.gov (United States)

    King, Zachary A; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A; Ebrahim, Ali; Palsson, Bernhard O; Lewis, Nathan E

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

  12. Noise source identification with the lattice Boltzmann method.

    Science.gov (United States)

    Vergnault, Etienne; Malaspinas, Orestis; Sagaut, Pierre

    2013-03-01

    In this paper the sound source identification problem is addressed with the use of the lattice Boltzmann method. To this aim, a time-reversed problem coupled to a complex differentiation method is used. In order to circumvent the inherent instability of the time-reversed lattice Boltzmann scheme, a method based on a split of the lattice Boltzmann equation into a mean and a perturbation component is used. Lattice Boltzmann method formulation around an arbitrary base flow is recalled and specific applications to acoustics are presented. The implementation of the noise source detection method for two-dimensional weakly compressible (low Mach number) flows is discussed, and the applicability of the method is demonstrated.

  13. Identification and characterization of microorganisms: DNA-fingerprinting methods

    Directory of Open Access Journals (Sweden)

    Md. Fakruddin

    2013-08-01

    Full Text Available Identification and classification of the microorganisms are of utmost importance in the field of environmental, industrial,medical and agricultural microbiology, and microbial ecology. Traditional phenotype-based methods encounter manychallenges and shortcomings which limit their usability. Molecular methods offer better solutions in identifying and characterizing microorganisms. Several DNA fingerprinting methods have been developed and are in use already. In principle, mostof these methods are based on PCR and restriction site analysis. Some of these methods are still not economic in use andrequire huge set-up cost. Continuous research is going on around the world to improve the methodology and applicability ofthese methods as well as to make them economic in use.

  14. Vortex Tube Modeling Using the System Identification Method

    Energy Technology Data Exchange (ETDEWEB)

    Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)

    2017-05-15

    In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.

  15. HUMAN FECAL SOURCE IDENTIFICATION: REAL-TIME QUANTITATIVE PCR METHOD STANDARDIZATION - abstract

    Science.gov (United States)

    Method standardization or the formal development of a protocol that establishes uniform performance benchmarks and practices is necessary for widespread adoption of a fecal source identification approach. Standardization of a human-associated fecal identification method has been...

  16. Human Fecal Source Identification: Real-Time Quantitative PCR Method Standardization

    Science.gov (United States)

    Method standardization or the formal development of a protocol that establishes uniform performance benchmarks and practices is necessary for widespread adoption of a fecal source identification approach. Standardization of a human-associated fecal identification method has been...

  17. An efficient method for identification of risk factors

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    This paper presents a method to identify risk factors during bridge construction.The method integrates the concepts of analytical hierarchy process and fuzzy consistent matrix method.The advantage of the method is that instead of using 9-point scale of relative importance of the conventional analytical hierarchy process,it uses a 3-point scale to describe the scale of importance,thus greatly simplifying the identification problem of risk factors.Moreover,the difficulties of making judgment and comparison caused by uncertainty that jeopardizes the accuracy of the results in the conventional analytical hierarchy process can also be overcome.Another advantage of the method is that it does not involve consistency checking,thus saving a large amount of CPU time.It has been demonstrated with a numerical example that the proposed fuzzy analytical hierarchy process based on 3-point scale can offer significant computational savings over the conventional analytical hierarchy process.

  18. Hazard identification by methods of animal-based toxicology.

    Science.gov (United States)

    Barlow, S M; Greig, J B; Bridges, J W; Carere, A; Carpy, A J M; Galli, C L; Kleiner, J; Knudsen, I; Koëter, H B W M; Levy, L S; Madsen, C; Mayer, S; Narbonne, J-F; Pfannkuch, F; Prodanchuk, M G; Smith, M R; Steinberg, P

    2002-01-01

    This paper is one of several prepared under the project "Food Safety In Europe: Risk Assessment of Chemicals in Food and Diet" (FOSIE), a European Commission Concerted Action Programme, organised by the International Life Sciences Institute, Europe (ILSI). The aim of the FOSIE project is to review the current state of the science of risk assessment of chemicals in food and diet, by consideration of the four stages of risk assessment, that is, hazard identification, hazard characterisation, exposure assessment and risk characterisation. The contribution of animal-based methods in toxicology to hazard identification of chemicals in food and diet is discussed. The importance of first applying existing technical and chemical knowledge to the design of safety testing programs for food chemicals is emphasised. There is consideration of the presently available and commonly used toxicity testing approaches and methodologies, including acute and repeated dose toxicity, reproductive and developmental toxicity, neurotoxicity, genotoxicity, carcinogenicity, immunotoxicity and food allergy. They are considered from the perspective of whether they are appropriate for assessing food chemicals and whether they are adequate to detect currently known or anticipated hazards from food. Gaps in knowledge and future research needs are identified; research on these could lead to improvements in the methods of hazard identification for food chemicals. The potential impact of some emerging techniques and toxicological issues on hazard identification for food chemicals, such as new measurement techniques, the use of transgenic animals, assessment of hormone balance and the possibilities for conducting studies in which common human diseases have been modelled, is also considered.

  19. Comparison of DNA Pyrosequencing with Alternative Methods for Identification of Mycobacteria▿

    OpenAIRE

    2008-01-01

    Identification of mycobacterial clinical isolates by pyrosequencing within the hypervariable A region of the 16S rRNA gene was compared to other identification methods. For >90% of isolates, these identifications correlated to the level of complex or species. For identification of many mycobacteria, pyrosequencing offers an inexpensive alternative to traditional sequencing.

  20. Identification of fractional order systems using modulating functions method

    KAUST Repository

    Liu, Dayan

    2013-06-01

    The modulating functions method has been used for the identification of linear and nonlinear systems. In this paper, we generalize this method to the on-line identification of fractional order systems based on the Riemann-Liouville fractional derivatives. First, a new fractional integration by parts formula involving the fractional derivative of a modulating function is given. Then, we apply this formula to a fractional order system, for which the fractional derivatives of the input and the output can be transferred into the ones of the modulating functions. By choosing a set of modulating functions, a linear system of algebraic equations is obtained. Hence, the unknown parameters of a fractional order system can be estimated by solving a linear system. Using this method, we do not need any initial values which are usually unknown and not equal to zero. Also we do not need to estimate the fractional derivatives of noisy output. Moreover, it is shown that the proposed estimators are robust against high frequency sinusoidal noises and the ones due to a class of stochastic processes. Finally, the efficiency and the stability of the proposed method is confirmed by some numerical simulations.

  1. An identification method for damping ratio in rotor systems

    Science.gov (United States)

    Wang, Weimin; Li, Qihang; Gao, Jinji; Yao, Jianfei; Allaire, Paul

    2016-02-01

    Centrifugal compressor testing with magnetic bearing excitations is the last step to assure the compressor rotordynamic stability in the designed operating conditions. To meet the challenges of stability evaluation, a new method combining the rational polynomials method (RPM) with the weighted instrumental variables (WIV) estimator to fit the directional frequency response function (dFRF) is presented. Numerical simulation results show that the method suggested in this paper can identify the damping ratio of the first forward and backward modes with high accuracy, even in a severe noise environment. Experimental tests were conducted to study the effect of different bearing configurations on the stability of rotor. Furthermore, two example centrifugal compressors (a nine-stage straight-through and a six-stage back-to-back) were employed to verify the feasibility of identification method in industrial configurations as well.

  2. Fragment Identification and Statistics Method of Hypervelocity Impact SPH Simulation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiaotian; JIA Guanghui; HUANG Hai

    2011-01-01

    A comprehensive treatment to the fragment identification and statistics for the smoothed particle hydrodynamics (SPH) simulation of hypervelocity impact is presented.Based on SPH method, combined with finite element method (FEM), the computation is performed.The fragments are identified by a new pre- and post-processing algorithm and then converted into a binary graph.The number of fragments and the attached SPH particles are determined by counting the quantity of connected domains on the binary graph.The size, velocity vector and mass of each fragment are calculated by the particles' summation and weighted average.The dependence of this method on finite element edge length and simulation terminal time is discussed.An example of tungsten rods impacting steel plates is given for calibration.The computation results match experiments well and demonstrate the effectiveness of this method.

  3. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

    NARCIS (Netherlands)

    Levering, J.; Fiedler, T.; Sieg, A.; van Grinsven, K.W.A.; Hering, S.; Veith, N.; Olivier, B.G.; Klett, L.; Hugenholtz, J.; Teusink, B.; Kreikemeyer, B.; Kummer, U.

    2016-01-01

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49

  4. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    Directory of Open Access Journals (Sweden)

    Jacek Puchałka

    2008-10-01

    Full Text Available A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile

  5. Method of Fire Image Identification Based on Optimization Theory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.

  6. Molecular identification of species in Juglandaceae:A tiered method

    Institute of Scientific and Technical Information of China (English)

    Xiao-Guo XIANG; Jing-Bo ZHANG; An-Ming LU; Rui-Qi LI

    2011-01-01

    DNA barcoding is a method of species identification and recognition using DNA sequence data. A tiered or multilocus method has been recommended for barcoding plant species. In this study, we sampled 196 individuals representing 9 genera and 54 species of Juglandaceae to investigate the utility of the four potential barcoding loci (rbcL, matK, trnH-psbA, and internal transcribed spacer (ITS)). Our results show that all four DNA regions are easy to amplify and sequence. In the four tested DNA regions, ITS has the most variable information, and rbcL has the least. At generic level, seven of nine genera can be efficiently identified by matK. At species level, ITS has higher interspecific p-distance than the trnH-psbA region. Difficult to align in the whole family, ITS showed heterogeneous variability among different genera. Except for the monotypic genera (Cyclocarya, Annamocarya, Platycarya), ITS appeared to have limited power for species identification within the Carya and Engelhardia complex, and have no power for Juglans or Pterocarya. Overall, our results confirmed that a multilocus tiered method for plant barcoding was applicable and practicable. With higher priority, matK is proposed as the first-tier DNA region for genus discrimination, and the second locus at species level should have enough stable variable characters.

  7. Damping identification in frequency domain using integral method

    Science.gov (United States)

    Guo, Zhiwei; Sheng, Meiping; Ma, Jiangang; Zhang, Wulin

    2015-03-01

    A new method for damping identification of linear system in frequency domain is presented, by using frequency response function (FRF) with integral method. The FRF curve is firstly transformed to other type of frequency-related curve by changing the representations of horizontal and vertical axes. For the newly constructed frequency-related curve, integral is conducted and the area forming from the new curve is used to determine the damping. Three different methods based on integral are proposed in this paper, which are called FDI-1, FDI-2 and FDI-3 method, respectively. For a single degree of freedom (Sdof) system, the formulated relation of each method between integrated area and loss factor is derived theoretically. The numeral simulation and experiment results show that, the proposed integral methods have high precision, strong noise resistance and are very stable in repeated measurements. Among the three integral methods, FDI-3 method is the most recommended because of its higher accuracy and simpler algorithm. The new methods are limited to linear system in which modes are well separated, and for closely spaced mode system, mode decomposition process should be conducted firstly.

  8. An Identification Method of Magnetizing Inrush Current Phenomena by Voltage Waveform

    Science.gov (United States)

    Naitoh, Tadashi; Takeda, Keiki; Toyama, Atsushi; Maeda, Tatsuhiko

    In this paper, the authors propose a new identification method of the magnetizing inrush current phenomena. In general, the identification is done using with current waveform. However, the saturation of current transformer can't give waveform. Therefore, the authors introduce the identification method using with voltage waveform, in which the saturation of voltage transformer doesn't happen. And then, applying the Aitken's Δ2-process, it is showed that the new identification method gives the exact saturation on/off time.

  9. Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

    Science.gov (United States)

    Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

    2016-12-01

    Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

  10. Systematic planning of genome-scale experiments in poorly studied species.

    Science.gov (United States)

    Guan, Yuanfang; Dunham, Maitreya; Caudy, Amy; Troyanskaya, Olga

    2010-03-05

    Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could

  11. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments.

    Science.gov (United States)

    Zhuang, Kai; Izallalen, Mounir; Mouser, Paula; Richter, Hanno; Risso, Carla; Mahadevan, Radhakrishnan; Lovley, Derek R

    2011-02-01

    The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.

  12. Rapid Prototyping of Microbial Cell Factories via Genome-scale Engineering

    Science.gov (United States)

    Si, Tong; Xiao, Han; Zhao, Huimin

    2014-01-01

    Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. PMID:25450192

  13. Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium.

    Science.gov (United States)

    D'Huys, Pieter-Jan; Lule, Ivan; Vercammen, Dominique; Anné, Jozef; Van Impe, Jan F; Bernaerts, Kristel

    2012-09-15

    Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly

  14. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

    DEFF Research Database (Denmark)

    Liu, Guodong; Marras, Antonio; Nielsen, Jens

    2014-01-01

    regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model......Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional...... transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner....

  15. Rapid prototyping of microbial cell factories via genome-scale engineering.

    Science.gov (United States)

    Si, Tong; Xiao, Han; Zhao, Huimin

    2015-11-15

    Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories.

  16. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    DEFF Research Database (Denmark)

    Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

    2013-01-01

    BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking...... using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p ... be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems....

  17. System Identification

    NARCIS (Netherlands)

    Keesman, K.J.

    2011-01-01

    Summary System Identification Introduction.- Part I: Data-based Identification.- System Response Methods.- Frequency Response Methods.- Correlation Methods.- Part II: Time-invariant Systems Identification.- Static Systems Identification.- Dynamic Systems Identification.- Part III: Time-varying

  18. Network output controllability-based method for drug target identification.

    Science.gov (United States)

    Wu, Lin; Shen, Yichao; Li, Min; Wu, Fang-Xiang

    2015-03-01

    Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.

  19. Variable identification in group method of data handling methodology

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil)

    2011-07-01

    The Group Method of Data Handling - GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a preselected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. A Monitoring and Diagnosis System was developed based on GMDH and Artificial Neural Network - ANN methodologies, and applied to the IPEN research Reactor IEA-R1. The GMDH was used to study the best set of variables to be used to train an ANN, resulting in a best monitoring variable estimative. The system performs the monitoring by comparing these estimative calculated values with measured ones. The IPEN Reactor Data Acquisition System is composed of 58 variables (process and nuclear variables). As the GMDH is a self-organizing methodology, the input variables choice is made automatically, and the real input variables used in the Monitoring and Diagnosis System were not showed in the final result. This work presents a study of variable identification of GMDH methodology by means of an algorithm that works in parallel with the GMDH algorithm and traces the initial variables paths, resulting in an identification of the variables that composes the best Monitoring and Diagnosis Model. (author)

  20. Variable identification in group method of data handling methodology

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil)

    2011-07-01

    The Group Method of Data Handling - GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a preselected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. A Monitoring and Diagnosis System was developed based on GMDH and Artificial Neural Network - ANN methodologies, and applied to the IPEN research Reactor IEA-R1. The GMDH was used to study the best set of variables to be used to train an ANN, resulting in a best monitoring variable estimative. The system performs the monitoring by comparing these estimative calculated values with measured ones. The IPEN Reactor Data Acquisition System is composed of 58 variables (process and nuclear variables). As the GMDH is a self-organizing methodology, the input variables choice is made automatically, and the real input variables used in the Monitoring and Diagnosis System were not showed in the final result. This work presents a study of variable identification of GMDH methodology by means of an algorithm that works in parallel with the GMDH algorithm and traces the initial variables paths, resulting in an identification of the variables that composes the best Monitoring and Diagnosis Model. (author)

  1. An overview of modal-based damage identification methods

    Energy Technology Data Exchange (ETDEWEB)

    Farrar, C.R.; Doebling, S.W. [Los Alamos National Lab., NM (United States). Engineering Analysis Group

    1997-09-01

    This paper provides an overview of methods that examine changes in measured vibration response to detect, locate, and characterize damage in structural and mechanical systems. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is first provided. The methods are then categorized according to various criteria such as the level of damage detection provided, model-based vs. non-model-based methods and linear vs. nonlinear methods. This overview is limited to methods that can be adapted to a wide range of structures (i.e., are not dependent on a particular assumed model form for the system such as beam-bending behavior and methods and that are not based on updating finite element models). Next, the methods are described in general terms including difficulties associated with their implementation and their fidelity. Past, current and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of modal-based damage identification.

  2. Dynamic system multivariate calibration by system identification methods

    Directory of Open Access Journals (Sweden)

    Rolf Ergon

    1998-04-01

    Full Text Available In the first part of the paper, the optimal estimator for normally nonmeasured primary outputs from a linear and time invariant dynamic system is developed. The estimator is based on an underlying Kalman filter, utilizing all available information in known inputs and measured secondary outputs. Assuming sufficient experimental data, the optimal estimator can be identified by specifying an output error model in a standard prediction error identification method. It is further shown that static estimators found by the ordinary least squares method or multivariate calibration by means of principal component regression (PCR or partial least squares regression (PLSR can be seen as special cases of the optimal dynamic estimator. Finally, it is shown that dynamic system PCR and PLSR solutions can be developed as special cases of the general estimator for dynamic systems.

  3. An online substructure identification method for local structural health monitoring

    Science.gov (United States)

    Hou, Jilin; Jankowski, Łukasz; Ou, Jinping

    2013-09-01

    This paper proposes a substructure isolation method, which uses time series of measured local response for online monitoring of substructures. The proposed monitoring process consists of two key steps: construction of the isolated substructure, and its identification. The isolated substructure is an independent virtual structure, which is numerically isolated from the global structure by placing virtual supports on the interface. First, the isolated substructure is constructed by a specific linear combination of time series of its measured local responses. Then, the isolated substructure is identified using its local natural frequencies extracted from the combined responses. The substructure is assumed to be linear; the outside part of the global structure can have any characteristics. The method has no requirements on the initial state of the structure, and so the process can be carried out repetitively for online monitoring. Online isolation and monitoring is illustrated in a numerical example with a frame model, and then verified in a cantilever beam experiment.

  4. Identification of groundwater parameters using an adaptative multiscale method.

    Science.gov (United States)

    Majdalani, Samer; Ackerer, Philippe

    2011-01-01

    The identification of groundwater parameters in heterogeneous systems is a major challenge in groundwater modeling. Flexible parameterization methods are needed to assess the complexity of the spatial distributions of these parameters in real aquifers. In this article, we introduce an adaptative parameterization to identify the distribution of hydraulic conductivity within the large-scale (4400 km(2) ) Upper Rhine aquifer. The method is based on adaptative multiscale triangulation (AMT) coupled with an inverse problem procedure that identifies the parameters' distributions by reducing the error between measured and simulated heads. The AMT method has the advantage of combining both zonation and interpolation approaches. The AMT method uses area-based interpolation rather than an interpolation based on stochastic features. The method is applied to a standard 2D groundwater model that takes into account the interactions between the aquifer and surface water bodies, groundwater recharge, and pumping wells. The simulation period covers 204 months, from January 1986 to December 2002. Recordings at 109 piezometers are used for model calibration. The simulated heads are globally quite accurate and reproduce the main dynamics of the system. The local hydraulic conductivities resulting from the AMT method agree qualitatively with existing local experimental observations across the Rhine aquifer.

  5. THEORETICAL MODEL AND NUMERICAL METHOD ON ONLINE IDENTIFICATION OF DYNAMICAL CHARACTERISTICS OF STRUCTURAL SYSTEM

    Institute of Scientific and Technical Information of China (English)

    姚志远; 汪凤泉

    2004-01-01

    An online method of identification of dynamic characteristics only using measured ambient response of structural dynamic system is widely focused on. The Ibrahim and ARMA (AutoRegressive Moving Average ) methods are basic identification methods. A model on dynamic system suffered by random ambient excitation was researched into, and a subspace decomposition method being different from traditional harmonic retrieval method was introduced. Robustness and effectiveness of this approach on identification of vibration characteristics are demonstrated on numerical experiment.

  6. About Shape Identification Methods of Objects Invariant to Projective Transformations

    Directory of Open Access Journals (Sweden)

    Gostev Ivan M.

    2016-01-01

    Full Text Available Diffculties concerning the choice of the invariants of the projective transformation groups used for the identification of the shapes of planar objects are illustrated and solutions allowing the derivation of robust identification criteria are discussed.

  7. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Directory of Open Access Journals (Sweden)

    Jennifer Levering

    Full Text Available Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  8. Genome-scale reconstruction of metabolic networks of Lactobacillus casei ATCC 334 and 12A.

    Directory of Open Access Journals (Sweden)

    Elena Vinay-Lara

    Full Text Available Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.

  9. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Science.gov (United States)

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  10. Genome-scale metabolic network of Cordyceps militaris useful for comparative analysis of entomopathogenic fungi.

    Science.gov (United States)

    Vongsangnak, Wanwipa; Raethong, Nachon; Mujchariyakul, Warasinee; Nguyen, Nam Ninh; Leong, Hon Wai; Laoteng, Kobkul

    2017-08-30

    The first genome-scale metabolic network of Cordyceps militaris (iWV1170) was constructed representing its whole metabolisms, which consisted of 894 metabolites and 1,267 metabolic reactions across five compartments, including the plasma membrane, cytoplasm, mitochondria, peroxisome and extracellular space. The iWV1170 could be exploited to explain its phenotypes of growth ability, cordycepin and other metabolites production on various substrates. A high number of genes encoding extracellular enzymes for degradation of complex carbohydrates, lipids and proteins were existed in C. militaris genome. By comparative genome-scale analysis, the adenine metabolic pathway towards putative cordycepin biosynthesis was reconstructed, indicating their evolutionary relationships across eleven species of entomopathogenic fungi. The overall metabolic routes involved in the putative cordycepin biosynthesis were also identified in C. militaris, including central carbon metabolism, amino acid metabolism (glycine, l-glutamine and l-aspartate) and nucleotide metabolism (adenosine and adenine). Interestingly, a lack of the sequence coding for ribonucleotide reductase inhibitor was observed in C. militaris that might contribute to its over-production of cordycepin. Copyright © 2017. Published by Elsevier B.V.

  11. A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

    Directory of Open Access Journals (Sweden)

    Loira Nicolas

    2012-05-01

    Full Text Available Abstract Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts.

  12. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

    Directory of Open Access Journals (Sweden)

    Motin Vladimir L

    2011-10-01

    Full Text Available Abstract Background Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Results Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Conclusions Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  13. Network thermodynamic curation of human and yeast genome-scale metabolic models.

    Science.gov (United States)

    Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

    2014-07-15

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties.

  14. An Experimentally-Supported Genome-Scale Metabolic Network Reconstruction for Yersinia pestis CO92

    Energy Technology Data Exchange (ETDEWEB)

    Charusanti, Pep; Chauhan, Sadhana; Mcateer, Kathleen; Lerman, Joshua A.; Hyduke, Daniel R.; Motin, Vladimir L.; Ansong, Charles; Adkins, Joshua N.; Palsson, Bernhard O.

    2011-10-13

    Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, thus provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  15. Bayesian Methods for Nonlinear System Identification of Civil Structures

    Directory of Open Access Journals (Sweden)

    Conte Joel P.

    2015-01-01

    Full Text Available This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF, the unscented Kalman filter (UKF, and the iterated extended Kalman filter (IEKF. For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF, the accurate and computationally efficient direct differentiation method (DDM is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF.

  16. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  17. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

    Science.gov (United States)

    Fang, Yilin; Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2011-03-01

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  18. Study of Biometric Identification Method Based on Naked Footprint

    Directory of Open Access Journals (Sweden)

    Raji Rafiu King

    2013-10-01

    Full Text Available The scale of deployment of biometric identity-verification systems has recently seen an enormous increase owing to the need for more secure and reliable way of identifying people. Footprint identification which can be defined as the measurement of footprint features for recognizing the identity of a user has surfaced recently. This study is based on a biometric personal identification method using static footprint features viz. friction ridge / texture and foot shape / silhouette. To begin with, naked footprints of users are captured; images then undergo pre processing followed by the extraction of two features; shape using Gradient Vector Flow (GVF) snake model and minutiae extraction respectively. Matching is then effected based on these two features followed by a fusion of these two results for either a reject or accept decision. Our shape matching feature is based on cosine similarity while the texture one is based on miniature score matching. The results from our research establish that the naked footprint is a credible biometric feature as two barefoot impressions of an individual match perfectly while that of two different persons shows a great deal of dissimilarity. Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Doi: 10.12777/ijse.5.2.29-35 How to cite this article: King

  19. Study on flow stability margin by method of system identification

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Youjie; Jiang Shengyao [Tsinghua Univ., Beijing, BJ (China). Inst. of Nuclear Energy Technology

    1999-11-01

    The main objective of the investigation is to develop a practical technology and method in engineering, based on general control theory, for distinguishing two-phase flow stability and identifying the safety margin by using the system identification method. By combining the two-phase flow stability theory in the thermo-physics field with the system stability theory and the system identification method in the field of information science, a thermo-hydraulic experiment technology with a new concept was developed. The experiment was carried out on the thermo-hydraulic test system HRTL-5 which serves as simulator to the primary circulation of the nuclear heating reactor NHR-5 and was used for investigation on its thermo-physical behavior. Reverse repeat pseudo-random sequences which were added to the steady heat flux as input signal sources and measured flow rates as response function were used in the test. The two-phase flow stability and the stability margin of the natural circulation system were investigated by analyzing the system pulse response function, the decay ratio and the stability boundary under different operational conditions. The results are compared with those obtained by using conventional methods. The test method and typical results obtained are presented in this paper. (orig.) [German] Das Hauptziel der Untersuchung ist die Entwicklung einer Technik und eines Verfahrens um - basierend auf allgemeiner Regelungstheorie - die Stabilitaet einer Zweiphasenstroemung zu bestimmen und unter Verwendung von Methoden zur Systemidentifikation Sicherheitsreserven zu ermitteln. Durch Kombination der Theorie der Zweiphasenstroemungsstabilitaet im Bereich der Thermophysik mit der Systemstabilitaetstheorie und der informationstheoretischen Systemidentifikationsmethode wurde eine thermohydraulische Experimentiertechnik neuartigen Konzepts entwickelt. Die Versuche wurden auf dem Thermohydraulikteststand HRTL-5 ausgefuehrt, der dem Primaeranlauf des Heizreaktors HHR-5

  20. iAK692: A genome-scale metabolic model of Spirulina platensis C1

    Science.gov (United States)

    2012-01-01

    Background Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform

  1. iAK692: A genome-scale metabolic model of Spirulina platensis C1

    Directory of Open Access Journals (Sweden)

    Klanchui Amornpan

    2012-06-01

    Full Text Available Abstract Background Spirulina (Arthrospira platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438 genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a

  2. Identification of sewage leaks by active remote-sensing methods

    Science.gov (United States)

    Goldshleger, Naftaly; Basson, Uri

    2016-04-01

    The increasing length of sewage pipelines, and concomitant risk of leaks due to urban and industrial growth and development is exposing the surrounding land to contamination risk and environmental harm. It is therefore important to locate such leaks in a timely manner, to minimize the damage. Advances in active remote sensing Ground Penetrating Radar (GPR) and Frequency Domain Electromagnetic (FDEM) technologies was used to identify leaking potentially responsible for pollution and to identify minor spills before they cause widespread damage. This study focused on the development of these electromagnetic methods to replace conventional acoustic methods for the identification of leaks along sewage pipes. Electromagnetic methods provide an additional advantage in that they allow mapping of the fluid-transport system in the subsurface. Leak-detection systems using GPR and FDEM are not limited to large amounts of water, but enable detecting leaks of tens of liters per hour, because they can locate increases in environmental moisture content of only a few percentage along the pipes. The importance and uniqueness of this research lies in the development of practical tools to provide a snapshot and monitoring of the spatial changes in soil moisture content up to depths of about 3-4 m, in open and paved areas, at relatively low cost, in real time or close to real time. Spatial measurements performed using GPR and FDEM systems allow monitoring many tens of thousands of measurement points per hectare, thus providing a picture of the spatial situation along pipelines and the surrounding. The main purpose of this study was to develop a method for detecting sewage leaks using the above-proposed geophysical methods, since their contaminants can severely affect public health. We focused on identifying, locating and characterizing such leaks in sewage pipes in residential and industrial areas.

  3. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Hojung Nam

    2014-09-01

    Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

  4. Moving image analysis to the cloud: A case study with a genome-scale tomographic study

    Science.gov (United States)

    Mader, Kevin; Stampanoni, Marco

    2016-01-01

    Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.

  5. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    DEFF Research Database (Denmark)

    Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

    2013-01-01

    BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking...... using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data...... available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about...

  6. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

    Science.gov (United States)

    Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.; Thiele, Ines; Palsson, Bernhard O.; Saunders, Michael A.

    2017-01-01

    Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

  7. Genome-scale analysis of positional clustering of mouse testis-specific genes

    Directory of Open Access Journals (Sweden)

    Lee Bernett TK

    2005-01-01

    Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

  8. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

    DEFF Research Database (Denmark)

    Hefzi, Hooman; Ang, Kok Siong; Hanscho, Michael

    2016-01-01

    in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production......Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways...... simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses....

  9. Genome scale models of yeast: towards standardized evaluation and consistent omic integration

    DEFF Research Database (Denmark)

    Sanchez, Benjamin J.; Nielsen, Jens

    2015-01-01

    Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published...... and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways...... in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted....

  10. Integration of gene expression data into genome-scale metabolic models

    DEFF Research Database (Denmark)

    Åkesson, M.; Förster, Jochen; Nielsen, Jens

    2004-01-01

    of gene expression from chemostat and batch cultures of Saccharomyces cerevisiae were combined with a recently developed genome-scale model, and the computed metabolic flux distributions were compared to experimental values from carbon labeling experiments and metabolic network analysis. The integration......A framework for integration of transcriptome data into stoichiometric metabolic models to obtain improved flux predictions is presented. The key idea is to exploit the regulatory information in the expression data to give additional constraints on the metabolic fluxes in the model. Measurements...... of expression data resulted in improved predictions of metabolic behavior in batch cultures, enabling quantitative predictions of exchange fluxes as well as qualitative estimations of changes in intracellular fluxes. A critical discussion of correlation between gene expression and metabolic fluxes is given....

  11. Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

    DEFF Research Database (Denmark)

    Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim;

    2008-01-01

    to a genome scale metabolic model of A. oryzae. Results: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted......Background: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number...... of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other...

  12. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

    DEFF Research Database (Denmark)

    Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna

    2015-01-01

    Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines...... based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted...... antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors...

  13. Genome-scale modeling of the protein secretory machinery in yeast.

    Science.gov (United States)

    Feizi, Amir; Österlund, Tobias; Petranovic, Dina; Bordel, Sergio; Nielsen, Jens

    2013-01-01

    The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery.

  14. Principles of proteome allocation are revealed using proteomic data and genome-scale models

    DEFF Research Database (Denmark)

    Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.

    2016-01-01

    , prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment...... of these sectors for the general stress response sigma factor sigma(S). Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally......Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked...

  15. Moving image analysis to the cloud: A case study with a genome-scale tomographic study

    Energy Technology Data Exchange (ETDEWEB)

    Mader, Kevin [4Quant Ltd., Switzerland & Institute for Biomedical Engineering at University and ETH Zurich (Switzerland); Stampanoni, Marco [Institute for Biomedical Engineering at University and ETH Zurich, Switzerland & Swiss Light Source at Paul Scherrer Institut, Villigen (Switzerland)

    2016-01-28

    Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.

  16. Primary Identification Methods and their Effectiveness in Mass Disaster Situations: A Literature Review

    Directory of Open Access Journals (Sweden)

    Naiara M. Gaglietti

    2017-06-01

    Full Text Available Mass disasters generally result in an elevated number of casualties that need identification. The primary identification methods listed by INTERPOL (DNA, fingerprint and forensic dentistry have a very important role in helping and speeding up the victim identification process. The present study sought to report mass destruction cases found in the literature published from 2005 to 2015 that have used the primary human identification methods. This study has been done as a literature review using the keywords: disasters, natural disasters, disaster victims, and human identification in a total of 16 selected papers and 13 listed disasters. It has been concluded that the primary identification methods are capable and efficient to perform a safe and satisfactory identification of mass disasters victims, used both separately or in combination.

  17. PIPI: PTM-Invariant Peptide Identification Using Coding Method.

    Science.gov (United States)

    Yu, Fengchao; Li, Ning; Yu, Weichuan

    2016-12-02

    In computational proteomics, the identification of peptides with an unlimited number of post-translational modification (PTM) types is a challenging task. The computational cost associated with database search increases exponentially with respect to the number of modified amino acids and linearly with respect to the number of potential PTM types at each amino acid. The problem becomes intractable very quickly if we want to enumerate all possible PTM patterns. To address this issue, one group of methods named restricted tools (including Mascot, Comet, and MS-GF+) only allow a small number of PTM types in database search process. Alternatively, the other group of methods named unrestricted tools (including MS-Alignment, ProteinProspector, and MODa) avoids enumerating PTM patterns with an alignment-based approach to localizing and characterizing modified amino acids. However, because of the large search space and PTM localization issue, the sensitivity of these unrestricted tools is low. This paper proposes a novel method named PIPI to achieve PTM-invariant peptide identification. PIPI belongs to the category of unrestricted tools. It first codes peptide sequences into Boolean vectors and codes experimental spectra into real-valued vectors. For each coded spectrum, it then searches the coded sequence database to find the top scored peptide sequences as candidates. After that, PIPI uses dynamic programming to localize and characterize modified amino acids in each candidate. We used simulation experiments and real data experiments to evaluate the performance in comparison with restricted tools (i.e., Mascot, Comet, and MS-GF+) and unrestricted tools (i.e., Mascot with error tolerant search, MS-Alignment, ProteinProspector, and MODa). Comparison with restricted tools shows that PIPI has a close sensitivity and running speed. Comparison with unrestricted tools shows that PIPI has the highest sensitivity except for Mascot with error tolerant search and Protein

  18. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism.

    Science.gov (United States)

    Vital-Lopez, Francisco G; Reifman, Jaques; Wallqvist, Anders

    2015-10-01

    A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm

  19. Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Amit Ghosh

    Full Text Available Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment.

  20. Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

    Energy Technology Data Exchange (ETDEWEB)

    Nagarajan, H; Sahin, M; Nogales, J; Latif, H; Lovley, DR; Ebrahim, A; Zengler, K

    2013-11-25

    Background: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H-2/CO2, and more importantly on synthesis gas (H-2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

  1. Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism.

    Science.gov (United States)

    Saha, Rajib; Suthers, Patrick F; Maranas, Costas D

    2011-01-01

    The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.

  2. Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism.

    Directory of Open Access Journals (Sweden)

    Rajib Saha

    Full Text Available The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize. Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular. GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3. The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.

  3. A hybrid method for identification of structural domains

    Science.gov (United States)

    Hua, Yongpan; Zhu, Min; Wang, Yuelong; Xie, Zhaoyang; Li, Menglong

    2014-12-01

    Structural domains in proteins are the basic units to form various proteins. In the protein's evolution and functioning, domains play important roles. But the definition of domain is not yet precisely given, and the update cycle of structural domain databases is long. The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise. Here, we present a method which recognizes the compact and modular segments of polypeptide chains to identify structural domains, and contrast some data sets to illuminate their effect. The method combines support vector machine (SVM) with K-means algorithm. It is faster and more stable than most current algorithms and performs better. It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties. We have developed a web-server, which would be helpful in identification of structural domains (http://vis.sculab.org/~huayongpan/cgi-bin/domainAssignment.cgi).

  4. [The choice of the study method in forensic medical osteological identification].

    Science.gov (United States)

    Abramov, S S

    1996-01-01

    Discusses the problems related to the theoretical validation of selecting a method of investigation, its application with due consideration for the algorithm of osteological identification, and assessment of the results of this or that method from the viewpoint of the objectiveness and informative value of the method for osteological identification.

  5. Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews

    Institute of Scientific and Technical Information of China (English)

    Junyong XIA; Youmin HU; Bo WU; Tielin SHI

    2008-01-01

    Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for ther-mal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.

  6. THEORETICAL ASPECTS AND METHODS OF PARAMETERS IDENTIFICATION OF ELECTRIC TRACTION SYSTEM DEVICES. METHOD OF WEIGHT FUNCTION

    Directory of Open Access Journals (Sweden)

    T. N. Mishchenko

    2014-10-01

    Full Text Available Purpose. Development and substantiation of a new method of structural identification of electrical devices of electric traction systems for both DC and AC current. Methodology. To solve this problem the following methods are used: the methods and techniques of the linear electrical engineering, in particular, the Laplace operator method; the numerical method for solving the integral equation, which is based on the representation of the Wiener-Hopf linear equations system (this allows forming the solutions of the problem in a mathematical form of the correlation and weight functions; the factorization method, which provides certain partition of the correlation functions of the stochastic processes. Findings. It was developed the method of weight function of the electrical devices identification, which can be fully used in the systems of electric traction. As the use example of the developed method it was considered a feeder section of DC electric traction with the single power supply. On this section move two electric locomotives of the type DE 1, they have been identified by the weighting functions. The required currents and voltages of electric locomotives are also formulated in the electric traction network in probabilistic and statistical form, that is, the functions of mathematical expectation and the correlation functions are determined. At this, it is taken into account that the correlation function of the sum of random functions is equal to the sum of the correlation functions of additives, and the correlation function of the integral of a random function is defined as the double integral of the correlation function of the output of a random function. Originality. Firstly, originality consists of the adaption of the developed method of structural identification for the devices of electric traction system. Secondly, it lies in the proper development of the new method of weight function. And finally, it lies in the solution of the Wiener

  7. Remarks on certain new methods for blind identification of FIR channels

    OpenAIRE

    P. P. Vaidyanathan; Su, Borching

    2004-01-01

    This paper discusses a number of issues pertaining to blind identification of channels. The basics of blind identification are first discussed and a method called Vandermonde method is presented which is based on elementary linear system principles. Then some remarks are made about precoders with paraunitary antipodal preprocessors. It is argued that such preprocessors usually destroy signal richness which is a necessary feature in blind identification systems.

  8. Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model

    NARCIS (Netherlands)

    Teusink, B.; Wiersma, A.; Molenaar, D.; Francke, C.; Vos, de W.M.; Siezen, R.J.; Smid, E.J.

    2006-01-01

    A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretati

  9. Using a genome-scale metabolic network model to elucidate the mechanism of chloroquine action in Plasmodium falciparum

    Directory of Open Access Journals (Sweden)

    Shivendra G. Tewari

    2017-08-01

    Full Text Available Chloroquine, long the default first-line treatment against malaria, is now abandoned in large parts of the world because of widespread drug-resistance in Plasmodium falciparum. In spite of its importance as a cost-effective and efficient drug, a coherent understanding of the cellular mechanisms affected by chloroquine and how they influence the fitness and survival of the parasite remains elusive. Here, we used a systems biology approach to integrate genome-scale transcriptomics to map out the effects of chloroquine, identify targeted metabolic pathways, and translate these findings into mechanistic insights. Specifically, we first developed a method that integrates transcriptomic and metabolomic data, which we independently validated against a recently published set of such data for Krebs-cycle mutants of P. falciparum. We then used the method to calculate the effect of chloroquine treatment on the metabolic flux profiles of P. falciparum during the intraerythrocytic developmental cycle. The model predicted dose-dependent inhibition of DNA replication, in agreement with earlier experimental results for both drug-sensitive and drug-resistant P. falciparum strains. Our simulations also corroborated experimental findings that suggest differences in chloroquine sensitivity between ring- and schizont-stage P. falciparum. Our analysis also suggests that metabolic fluxes that govern reduced thioredoxin and phosphoenolpyruvate synthesis are significantly decreased and are pivotal to chloroquine-based inhibition of P. falciparum DNA replication. The consequences of impaired phosphoenolpyruvate synthesis and redox metabolism are reduced carbon fixation and increased oxidative stress, respectively, both of which eventually facilitate killing of the parasite. Our analysis suggests that a combination of chloroquine (or an analogue and another drug, which inhibits carbon fixation and/or increases oxidative stress, should increase the clearance of P

  10. A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation.

    Science.gov (United States)

    Dufault-Thompson, Keith; Jian, Huahua; Cheng, Ruixue; Li, Jiefu; Wang, Fengping; Zhang, Ying

    2017-01-01

    Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation

  11. A METHOD OF ONLINE DAMAGE IDENTIFICATION FOR STRUCTURES BASED ON AMBIENT VIBRATION

    Institute of Scientific and Technical Information of China (English)

    YAO Zhi-yuan; WANG Feng-quan; ZHAO Chun-sheng

    2005-01-01

    A method of damage identification for engineering structures based on ambient vibration is put forward, in which output data are used only. Firstly, it was identification of the statistic parameters to associate with the exterior excitation for undamaged structures.Then it was detection and location of the structural damages for damaged structures. The ambient identification method includes a theoretical model and numerical method. The numerical experiment results show the method is precise and effective. This method may be used in health monitoring for bridges and architectures.

  12. Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

    Directory of Open Access Journals (Sweden)

    Caspeta Luis

    2012-04-01

    Full Text Available Abstract Background Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. Results In this work, genome-scale metabolic models (GEMs of P. stipitis (iSS884 and P. pastoris (iLC915 were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. Conclusions In conclusion the first GEM of P. stipitis (iSS884 was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.

  13. Mycobacteria mobility shift assay: a method for the rapid identification of Mycobacterium tuberculosis and nontuberculous mycobacteria

    Directory of Open Access Journals (Sweden)

    Letícia Muraro Wildner

    2014-06-01

    Full Text Available The identification of mycobacteria is essential because tuberculosis (TB and mycobacteriosis are clinically indistinguishable and require different therapeutic regimens. The traditional phenotypic method is time consuming and may last up to 60 days. Indeed, rapid, affordable, specific and easy-to-perform identification methods are needed. We have previously described a polymerase chain reaction-based method called a mycobacteria mobility shift assay (MMSA that was designed for Mycobacterium tuberculosis complex (MTC and nontuberculous mycobacteria (NTM species identification. The aim of this study was to assess the MMSA for the identification of MTC and NTM clinical isolates and to compare its performance with that of the PRA-hsp65 method. A total of 204 clinical isolates (102 NTM and 102 MTC were identified by the MMSA and PRA-hsp65. For isolates for which these methods gave discordant results, definitive species identification was obtained by sequencing fragments of the 16S rRNA and hsp65 genes. Both methods correctly identified all MTC isolates. Among the NTM isolates, the MMSA alone assigned 94 (92.2% to a complex or species, whereas the PRA-hsp65 method assigned 100% to a species. A 91.5% agreement was observed for the 94 NTM isolates identified by both methods. The MMSA provided correct identification for 96.8% of the NTM isolates compared with 94.7% for PRA-hsp65. The MMSA is a suitable auxiliary method for routine use for the rapid identification of mycobacteria.

  14. On-line methods for rotorcraft aeroelastic mode identification

    Science.gov (United States)

    Molusis, J. A.; Kleinman, D. L.

    1982-01-01

    The requirements for the on-line identification of rotorcraft aeroelastic blade modes from random response test data are presented. A recursive maximum likelihood (RML) technique is used in conjunction with a bandpass filter to identify isolated blade mode damping and frequency. The RML technique is demonstrated to have excellent convergence characteristics in random measurement noise and random process noise excitation. The RML identification technique uses an ARMA representation for the aeroelastic stochastic system and requires virtually no user interaction while providing accurate confidence bands on the parameter estimates. Comparisons are made with an off-line Newton type maximum likelihood algorithm which uses a state variable model representation. Results are presented from simulation random response data which quantify the identifed parameter convergence behavior for various levels of random excitation which is typical of wind tunnel turbulence levels. The RML technique is applied to hingless rotor test data from the NASA Langley Research Center Helicopter Hover Facility.

  15. Functional states of the genome-scale Escherichia coli transcriptional regulatory system.

    Directory of Open Access Journals (Sweden)

    Erwin P Gianchandani

    2009-06-01

    Full Text Available A transcriptional regulatory network (TRN constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS] and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1 a gene expression correlation matrix delineating functional motifs; (2 sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3 the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

  16. Functional states of the genome-scale Escherichia coli transcriptional regulatory system.

    Science.gov (United States)

    Gianchandani, Erwin P; Joyce, Andrew R; Palsson, Bernhard Ø; Papin, Jason A

    2009-06-01

    A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

  17. Further developments towards a genome-scale metabolic model of yeast

    Directory of Open Access Journals (Sweden)

    Dunn Warwick B

    2010-10-01

    Full Text Available Abstract Background To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity. Results We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions. Conclusions We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

  18. Expanding a dynamic flux balance model of yeast fermentation to genome-scale

    Directory of Open Access Journals (Sweden)

    Agosin Eduardo

    2011-05-01

    Full Text Available Abstract Background Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints. Results Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol. Conclusion A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations.

  19. Genome-Scale Metabolic Modeling in the Simulation of Field-Scale Uranium Bioremediation

    Science.gov (United States)

    Yabusaki, S.; Wilkins, M.; Fang, Y.; Williams, K. H.; Waichler, S.; Long, P. E.

    2015-12-01

    Coupled variably saturated flow and biogeochemical reactive transport modeling is used to improve understanding of the processes, properties, and conditions controlling uranium bio-immobilization in a field experiment where uranium-contaminated groundwater was amended with acetate and bicarbonate. The acetate stimulates indigenous microorganisms that catalyze metal reduction, including the conversion of aqueous U(VI) to solid-phase U(IV), which effectively removes uranium from solution. The initiation of the bicarbonate amendment prior to biostimulation was designed to promote U(VI) desorption that would increase the aqueous U(VI) available for bioreduction. The three-dimensional simulations were able to largely reproduce the timing and magnitude of the physical, chemical and biological responses to the acetate and bicarbonate amendment in the context of changing water table elevation and gradient. A time series of groundwater proteomic samples exhibited correlations between the most abundant Geobacter metallireducens proteins and the genome-scale metabolic model-predicted fluxes of intra-cellular reactions associated with each of those proteins. The desorption of U(VI) induced by the bicarbonate amendment led to initially higher rates of bioreduction compared to locations with minimal bicarbonate exposure. After bicarbonate amendment ceased, bioreduction continued at these locations whereas U(VI) sorption was the dominant removal mechanism at the bicarbonate-impacted sites.

  20. Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

    Science.gov (United States)

    2017-01-01

    Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria. PMID:28133437

  1. Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

    Directory of Open Access Journals (Sweden)

    Hong Yang

    2014-09-01

    Full Text Available Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA. TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1 previous genome-wide direct gene-essentiality assignments; and, 2 flux balance analysis (FBA predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.

  2. Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum.

    Science.gov (United States)

    Subramanian, Abhishek; Sarkar, Ram Rup

    2017-08-31

    Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.

  3. Genome scale evolution of myxoma virus reveals host-pathogen adaptation and rapid geographic spread.

    Science.gov (United States)

    Kerr, Peter J; Rogers, Matthew B; Fitch, Adam; Depasse, Jay V; Cattadori, Isabella M; Twaddle, Alan C; Hudson, Peter J; Tscharke, David C; Read, Andrew F; Holmes, Edward C; Ghedin, Elodie

    2013-12-01

    The evolutionary interplay between myxoma virus (MYXV) and the European rabbit (Oryctolagus cuniculus) following release of the virus in Australia in 1950 as a biological control is a classic example of host-pathogen coevolution. We present a detailed genomic and phylogeographic analysis of 30 strains of MYXV, including the Australian progenitor strain Standard Laboratory Strain (SLS), 24 Australian viruses isolated from 1951 to 1999, and three isolates from the early radiation in Britain from 1954 and 1955. We show that in Australia MYXV has spread rapidly on a spatial scale, with multiple lineages cocirculating within individual localities, and that both highly virulent and attenuated viruses were still present in the field through the 1990s. In addition, the detection of closely related virus lineages at sites 1,000 km apart suggests that MYXV moves freely in geographic space, with mosquitoes, fleas, and rabbit migration all providing means of transport. Strikingly, despite multiple introductions, all modern viruses appear to be ultimately derived from the original introductions of SLS. The rapidity of MYXV evolution was also apparent at the genomic scale, with gene duplications documented in a number of viruses. Duplication of potential virulence genes may be important in increasing the expression of virulence proteins and provides the basis for the evolution of novel functions. Mutations leading to loss of open reading frames were surprisingly frequent and in some cases may explain attenuation, but no common mutations that correlated with virulence or attenuation were identified.

  4. A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189.

    Directory of Open Access Journals (Sweden)

    Patrick F Suthers

    2009-02-01

    Full Text Available With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites, is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

  5. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

    KAUST Repository

    Hefzi, Hooman

    2016-11-23

    Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

  6. On the road to synthetic life: the minimal cell and genome-scale engineering.

    Science.gov (United States)

    Juhas, Mario

    2016-01-01

    Synthetic biology employs rational engineering principles to build biological systems from the libraries of standard, well characterized biological parts. Biological systems designed and built by synthetic biologists fulfill a plethora of useful purposes, ranging from better healthcare and energy production to biomanufacturing. Recent advancements in the synthesis, assembly and "booting-up" of synthetic genomes and in low and high-throughput genome engineering have paved the way for engineering on the genome-wide scale. One of the key goals of genome engineering is the construction of minimal genomes consisting solely of essential genes (genes indispensable for survival of living organisms). Besides serving as a toolbox to understand the universal principles of life, the cell encoded by minimal genome could be used to build a stringently controlled "cell factory" with a desired phenotype. This review provides an update on recent advances in the genome-scale engineering with particular emphasis on the engineering of minimal genomes. Furthermore, it presents an ongoing discussion to the scientific community for better suitability of minimal or robust cells for industrial applications.

  7. Identification methods for Campylobacters, Helicobacters, and related organisms

    DEFF Research Database (Denmark)

    On, Stephen L.W.

    1996-01-01

    for furthering our understanding of their pathology and epidemiology. However, the identification process is made difficult because of the complex and rapidly evolving taxonomy, fastidious nature, and biochemical inertness of these bacteria. These problems have resulted in a proliferation of phenotypic...

  8. A new method of choosing scales in wavelet transform for damping identification

    Institute of Scientific and Technical Information of China (English)

    HE Rui; LUO Wen-bo; WANG Ben-li

    2008-01-01

    A systematic study of the method of selecting scales in wavelet transform for damping identification in frequency domain was carried out. A method to select the scale with the modulus at the maximum was developed by extending the range of scales, it is proved that using this method in small damping ratio and linear system,we can achieve better results in identification of the closely-spaced model.

  9. Improved fuzzy identification method based on Hough transformation and fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    刘福才; 路平立; 潘江华; 裴润

    2004-01-01

    This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

  10. Thruster Modelling for Underwater Vehicle Using System Identification Method

    Directory of Open Access Journals (Sweden)

    Mohd Shahrieel Mohd Aras

    2013-05-01

    Full Text Available This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.

  11. Thruster Modelling for Underwater Vehicle Using System Identification Method

    Directory of Open Access Journals (Sweden)

    Mohd Shahrieel Mohd Aras

    2013-05-01

    Full Text Available Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.

  12. Determining the control circuitry of redox metabolism at the genome-scale.

    Directory of Open Access Journals (Sweden)

    Stephen Federowicz

    2014-04-01

    Full Text Available Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs, ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2 (p<1e-6 correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.

  13. A Genome-Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium.

    Science.gov (United States)

    Ribaudo, Nicholas; Li, Xianhua; Davis, Brett; Wood, Thomas K; Huang, Zuyi Jacky

    2017-01-01

    Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h(-1) respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

  14. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation.

    Science.gov (United States)

    Racle, Julien; Stefaniuk, Adam Jan; Hatzimanikatis, Vassily

    2015-07-28

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  15. Genome-Scale Assessment of Age-Related DNA Methylation Changes in Mouse Spermatozoa

    Science.gov (United States)

    Kobayashi, Norio; Okae, Hiroaki; Hiura, Hitoshi; Chiba, Hatsune; Shirakata, Yoshiki; Hara, Kenshiro; Tanemura, Kentaro; Arima, Takahiro

    2016-01-01

    DNA methylation plays important roles in the production and functioning of spermatozoa. Recent studies have suggested that DNA methylation patterns in spermatozoa can change with age, but the regions susceptible to age-related methylation changes remain to be fully elucidated. In this study, we conducted genome-scale DNA methylation profiling of spermatozoa obtained from C57BL/6N mice at 8 weeks (8w), 18 weeks (18w) and 17 months of age (17m). There was no substantial difference in the global DNA methylation patterns between 18w and 17m samples except for a slight increase of methylation levels in long interspersed nuclear elements in the 17m samples. We found that maternally methylated imprinting control regions (mICRs) and spermatogenesis-related gene promoters had 5–10% higher methylation levels in 8w samples than in 18w or 17m samples. Analysis of individual sequence reads suggested that these regions were fully methylated (80–100%) in a subset of 8w spermatozoa. These regions are also known to be highly methylated in a subset of postnatal spermatogonia, which might be the source of the increased DNA methylation in 8w spermatozoa. Another possible source was contamination by somatic cells. Although we carefully purified the spermatozoa, it was difficult to completely exclude the possibility of somatic cell contamination. Further studies are needed to clarify the source of the small increase in DNA methylation in the 8w samples. Overall, our findings suggest that DNA methylation patterns in mouse spermatozoa are relatively stable throughout reproductive life. PMID:27880848

  16. GMATA: an integrated software package for genome-scale SSR mining, marker development and viewing

    Directory of Open Access Journals (Sweden)

    Xuewen Wang

    2016-09-01

    Full Text Available Simple sequence repeats (SSRs, also referred to as microsatellites, are highly variable tandem DNAs that are widely used as genetic markers. The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at http://sourceforge.net/projects/gmata/?source=navbar.

  17. GMATA: An Integrated Software Package for Genome-Scale SSR Mining, Marker Development and Viewing

    Science.gov (United States)

    Wang, Xuewen; Wang, Le

    2016-01-01

    Simple sequence repeats (SSRs), also referred to as microsatellites, are highly variable tandem DNAs that are widely used as genetic markers. The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA) integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at http://sourceforge.net/projects/gmata/?source=navbar. PMID:27679641

  18. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

    Energy Technology Data Exchange (ETDEWEB)

    Racle, Julien; Hatzimanikatis, Vassily, E-mail: vassily.hatzimanikatis@epfl.ch [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland); Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne (Switzerland); Stefaniuk, Adam Jan [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland)

    2015-07-28

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  19. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

    OpenAIRE

    Feist Adam M; Bordbar Aarash; Usaite-Black Renata; Woodcock Joseph; Palsson Bernhard O; Famili Iman

    2011-01-01

    Abstract Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown ut...

  20. A new identification method for energetic ion {delta}E-E telescopes

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Cesar [Departamento de Fisica, Universidad de Alcala, 28871 Alcala de Henares, Madrid (Spain); Institut fuer Experimentelle und Angewandte Physik, Christian-Albrechts, Universitaet zu Kiel, 24118 Kiel (Germany); Bronchalo, Enrique [Departamento de Fisica, Universidad de Alcala, 28871 Alcala de Henares, Madrid (Spain); Departamento de Fisica y Arquitectura de Computadores, Universidad Miguel Hernandez, Avda. Universidad s/n, 03202 Elche, Alicante (Spain)], E-mail: ebronchalo@umh.es; Medina, Jose [Departamento de Fisica, Universidad de Alcala, 28871 Alcala de Henares, Madrid (Spain)

    2007-11-21

    A new ion identification method for {delta}E-E telescopes is presented. The method works by counting data points under {delta}E(E) curves on {delta}E-E diagrams. These curves are obtained by simulating the telescope response to a flux of energetic ions. The method is checked against three published methods applied to several experimental data sets.

  1. A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features.

    Science.gov (United States)

    Jin, Taisong; Hou, Xueliang; Li, Pifan; Zhou, Feifei

    2015-01-01

    Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.

  2. Image De-Identification Methods for Clinical Research in the XDS Environment.

    Science.gov (United States)

    Aryanto, K Y E; van Kernebeek, G; Berendsen, B; Oudkerk, M; van Ooijen, P M A

    2016-04-01

    To investigate possible de-identification methodologies within the Cross-Enterprise Document Sharing for imaging (XDS-I) environment in order to provide strengthened support for image data exchange as part of clinical research projects. De-identification, using anonymization or pseudonymization, is the most common method to perform information removal within DICOM data. However, it is not a standard part of the XDS-I profiles. Different methodologies were observed to define how and where de-identification should take place within an XDS environment used for scientific research. De-identification service can be placed in three locations within the XDS-I framework: 1) within the Document Source, 2) between the Document Source and Document Consumer, and 3) within the Document Consumer. First method has a potential advantage with respect to the exposure of the images to outside systems but has drawbacks with respect to additional hardware and configuration requirements. Second and third method have big concern in exposing original documents with all identifiable data being intact after leaving the Document Source. De-identification within the Document Source has more advantages compared to the other methods. On the contrary, it is less recommended to perform de-identification within the Document Consumer since it has the highest risk of the exposure of patients identity due to the fact that images are exposed without de-identification during the transfers.

  3. RRW: repeated random walks on genome-scale protein networks for local cluster discovery

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2009-09-01

    Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.

  4. Development of methods for identification of phenolic compounds in tansy flowers

    Directory of Open Access Journals (Sweden)

    Маргарита Юріївна Золотайкіна

    2016-04-01

    Full Text Available Tanacetum vulgare L. is widespread herb in Ukraine, having a substantial resource base. The absence of national normative documentation for this type of herbal material points to the relevance of research in this area and development of modern methods for identification of phenolic compounds as a main group of biologically active substances.Aim. Development of harmonized with European Pharmacopoeia (PhEur requirements method for identification of phenolic compounds in Tansy flowers.Methods. For the purpose mentioned above, a standardized TLC-method described in the State Pharmacopoeia of Ukraine (SPhU monographs for identification of phenolic compounds in different types of herbal material was used. Validation parameters of the method for the studied herb were determined.Results. A TLC method for identification of phenolic compounds in Tansy flowers considering modern PhEur approaches to standardization of herbs was developed. Validation of the identification method was carried out for the following validation parameters: specificity, robustness, and precision. This method was offered for implementation to the SPhU monograph «Tansy flowers».Conclusion. TLC method for identification of phenolic compounds in Tansy flowers was developed for the first time. As a result of validation of the given method for identification of phenolic compounds, by validation parameters (specificity, robustness, and precision, following chromatography conditions were selected: Silica gel coated Aluminum-backed TLC plates, application volume for both test samples and reference samples was 10 μl, mobile phase: formic acid concentrated – water – methyl ethyl ketone – ethyl acetate (10:10:30:50 V/V/V/V, detection – spraying with solutions of aminoethyl diphenylborinate in methanol and macrogol in methanol and view under 365 nm UV. The method for quantitative determination of phenolic compounds in Tansy flowers was offered for implementation in «Tansy flowers

  5. Application of a new method of nonlinear dynamical system identification to biochemical problems.

    Science.gov (United States)

    Karnaukhov, A V; Karnaukhova, E V

    2003-03-01

    The system identification method for a variety of nonlinear dynamic models is elaborated. The problem of identification of an original nonlinear model presented as a system of ordinary differential equations in the Cauchy explicit form with a polynomial right part reduces to the solution of the system of linear equations for the constants of the dynamical model. In other words, to construct an integral model of the complex system (phenomenon), it is enough to collect some data array characterizing the time-course of dynamical parameters of the system. Collection of such a data array has always been a problem. However difficulties emerging are, as a rule, not principal and may be overcome almost without exception. The potentialities of the method under discussion are demonstrated by the example of the test problem of multiparametric nonlinear oscillator identification. The identification method proposed may be applied to the study of different biological systems and in particular the enzyme kinetics of complex biochemical reactions.

  6. Expression induction of P450 genes by imidacloprid in Nilaparvata lugens: A genome-scale analysis.

    Science.gov (United States)

    Zhang, Jianhua; Zhang, Yixi; Wang, Yunchao; Yang, Yuanxue; Cang, Xinzhu; Liu, Zewen

    2016-09-01

    The overexpression of P450 monooxygenase genes is a main mechanism for the resistance to imidacloprid, a representative neonicotinoid insecticide, in Nilaparvata lugens (brown planthopper, BPH). However, only two P450 genes (CYP6AY1 and CYP6ER1), among fifty-four P450 genes identified from BPH genome database, have been reported to play important roles in imidacloprid resistance until now. In this study, after the confirmation of important roles of P450s in imidacloprid resistance by the synergism analysis, the expression induction by imidacloprid was determined for all P450 genes. In the susceptible (Sus) strain, eight P450 genes in Clade4, eight in Clade3 and two in Clade2 were up-regulated by imidacloprid, among which three genes (CYP6CS1, CYP6CW1 and CYP6ER1, all in Clade3) were increased to above 4.0-fold and eight genes to above 2.0-fold. In contrast, no P450 genes were induced in Mito clade. Eight genes induced to above 2.0-fold were selected to determine their expression and induced levels in Huzhou population, in which piperonyl butoxide showed the biggest effects on imidacloprid toxicity among eight field populations. The expression levels of seven P450 genes were higher in Huzhou population than that in Sus strain, with the biggest differences for CYP6CS1 (9.8-fold), CYP6ER1 (7.7-fold) and CYP6AY1 (5.1-fold). The induction levels for all tested genes were bigger in Sus strain than that in Huzhou population except CYP425B1. Screening the induction of P450 genes by imidacloprid in the genome-scale will provide an overall view on the possible metabolic factors in the resistance to neonicotinoid insecticides. The further work, such as the functional study of recombinant proteins, will be performed to validate the roles of these P450s in imidacloprid resistance.

  7. A novel method for single bacteria identification by Raman spectroscopy

    Science.gov (United States)

    Schultz, Emmanuelle; Simon, Anne-Catherine; Strola, Samy Andrea; Perenon, Rémi; Espagnon, Isabelle; Allier, Cédric; Claustre, Patricia; Jary, Dorothée.; Dinten, Jean-Marc

    2014-03-01

    In this paper we present results on single bacteria rapid identification obtained with a low-cost and compact Raman spectrometer. At present, we demonstrate that a 1 minute procedure, including the localization of single bacterium, is sufficient to acquire comprehensive Raman spectrum in the range of 600 to 3300 cm-1. Localization and detection of single bacteria is performed by means of lensfree imaging over a large field of view of 24 mm2. An excitation source of 532 nm and 30 mW illuminates single bacteria to collect Raman signal into a Tornado Spectral Systems prototype spectrometer (HTVS technology). The acquisition time to record a single bacterium spectrum is as low as 10 s owing to the high light throughput of this spectrometer. The spectra processing features different steps for cosmic spikes removal, background subtraction, and gain normalization to correct the residual inducted fluorescence and substrate fluctuations. This allows obtaining a fine chemical fingerprint analysis. We have recorded a total of 1200 spectra over 7 bacterial species (E. coli, Bacillus species, S. epidermis, M. luteus, S. marcescens). The analysis of this database results in a high classification score of almost 90 %. Hence we can conclude that our setup enables automatic recognition of bacteria species among 7 different species. The speed and the sensitivity (<30 minutes for localization and spectra collection of 30 single bacteria) of our Raman spectrometer pave the way for high-throughput and non-destructive real-time bacteria identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic and environmental applications.

  8. A TRUST REGION METHOD FOR SOLVING DISTRIBUTED PARAMETER IDENTIFICATION PROBLEMS

    Institute of Scientific and Technical Information of China (English)

    Yan-fei Wang; Ya-xiang Yuan

    2003-01-01

    This paper is concerned with the ill-posed problems of identifying a parameter in an elliptic equation which appears in many applications in science and industry. Its solution is obtained by applying trust region method to a nonlinear least squares error problem.Trust region method has long been a popular method for well-posed problems. This paper indicates that it is also suitable for ill-posed problems. Numerical experiment is given to compare the trust region method with the Tikhonov regularization method. It seems that the trust region method is more promising.

  9. Methods for Wild Pig Identifications from Moving Pictures and Discrimination of Female Wild Pigs based on Feature Matching Methods

    OpenAIRE

    Kohei Arai; Indra Nugraha Abdullah; Kensuke Kubo; Katsumi Sugawa

    2015-01-01

    Methods for wild pig identifications and discrimination of female wild pigs based on feature matching methods with acquired Near Infrared: NIR moving pictures are proposed. Trials and errors are repeated for identifying wild pigs and for discrimination of female wild pigs through experiments. As a conclusion, feature matching methods with the target nipple features show a better performance. Feature matching method of FLANN shows the best performance in terms of feature extraction and trackin...

  10. Methods for Wild Pig Identifications from Moving Pictures and Discrimination of Female Wild Pigs based on Feature Matching Methods

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2015-07-01

    Full Text Available Methods for wild pig identifications and discrimination of female wild pigs based on feature matching methods with acquired Near Infrared: NIR moving pictures are proposed. Trials and errors are repeated for identifying wild pigs and for discrimination of female wild pigs through experiments. As a conclusion, feature matching methods with the target nipple features show a better performance. Feature matching method of FLANN shows the best performance in terms of feature extraction and tracking capabilities.

  11. IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

    Science.gov (United States)

    Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

    2017-07-24

    Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

  12. A State Space Method for Modal Identification of Mechanical Systems from Time Domain Responses

    Directory of Open Access Journals (Sweden)

    Xiaobo Liu

    2005-01-01

    Full Text Available A new state space method is presented for modal identification of a mechanical system from its time domain impulse or initial condition responses. A key step in this method is the identification of the characteristic polynomial coefficients of an adjoint system. Once these coefficients are determined, a canonical state space realization of the adjoint system and the system's modal parameters are formulated straightforwardly. This method is conceptually and mathematically simple and is easy to be implemented. Detailed mathematical treatments are demonstrated and numerical examples are provided to illustrate the use and effectiveness of the method.

  13. Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification (SSI-COV) method

    Science.gov (United States)

    Xie, Yong; Liu, Pan; Cai, Guo-Ping

    2016-08-01

    In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification (SSI-COV) algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally, numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.

  14. Identification of the parameters of an elastic material model using the constitutive equation gap method

    KAUST Repository

    Florentin, Éric

    2010-04-23

    Today, the identification ofmaterialmodel parameters is based more and more on full-field measurements. This article explains how an appropriate use of the constitutive equation gap method (CEGM) can help in this context. The CEGM is a well-known concept which, until now, has been used mainly for the verification of finite element simulations. This has led to many developments, especially concerning the techniques for constructing statically admissible stress fields. The originality of the present study resides in the application of these recent developments to the identification problem. The proposed CEGM is described in detail, then evaluated through the identification of heterogeneous isotropic elastic properties. The results obtained are systematically compared with those of the equilibrium gap method, which is a well-known technique for the resolution of such identification problems. We prove that the use of the enhanced CEGM significantly improves the quality of the results. © Springer-Verlag 2010.

  15. A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics.

    Science.gov (United States)

    Stanley, Jeffrey R; Adkins, Joshua N; Slysz, Gordon W; Monroe, Matthew E; Purvine, Samuel O; Karpievitch, Yuliya V; Anderson, Gordon A; Smith, Richard D; Dabney, Alan R

    2011-08-15

    Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

  16. Realizations of BELS as WIV method in both direct and indirect closed-loop system identification

    Institute of Scientific and Technical Information of China (English)

    JIA LiJuan; TAO Ran; WANG Yue; WADA Kiyoshi

    2009-01-01

    The bias eliminated least squares (BELS) method, which Is known as efficient for unknown parameter estimation of transfer function In the correlated noise case, has been developed and applied effectively to the closed-loop system identification. In this paper, under the general settings, the realizations of the BELS method as a weighted instrumental variables (WIV) method in both direct and indirect closed-loop system identification are established through constructing an appropriate weighting matrix in the WIV method. The constructed structures are similar in both cases, which reveals that all the proof procedures of the two realizations are the same. Thus, the unified realizations of the BELS as the WIV method for the closed-loop system identification can be built. A simulation example Is given to validate our theoretical analysis.

  17. Identification of heterogeneous elastic material characteristics by virtual fields method

    Science.gov (United States)

    Sato, Yuya; Arikawa, Shuichi; Yoneyama, Satoru

    2015-03-01

    In this study, a method for identifying the elastic material characteristics of a heterogeneous material from measured displacements is proposed. The virtual fields method is employed for determining the elastic material characteristics. The solid propellant is considered as heterogeneous materials for the test subject. An equation representing the distribution of the material properties of the solid propellant is obtained by Fick's law, and the distribution is applied to the virtual fields method. The effectiveness of the proposed method is demonstrated by applying to displacement fields obtained using finite element analysis. Results show that the heterogeneous material properties can be obtained by the proposed method.

  18. On-line Identification of the DC motor Parameters by using Least Mean Square Recursive Method

    Directory of Open Access Journals (Sweden)

    Marian Gaiceanu

    2014-09-01

    Full Text Available The on-line least square parameters (electrical and mechanical identification method of the separately DC motor is presented in this paper. The parameters of the DC machine are time dependent; therefore, for the DC drive control the on-line parameters identification procedure is mandatory. By knowing accurately the DC motor parameters, the parameters of the DC speed and current cascade controllers can be obtained.

  19. A Markov Chain Monte Carlo Based Method for System Identification

    Energy Technology Data Exchange (ETDEWEB)

    Glaser, R E; Lee, C L; Nitao, J J; Hanley, W G

    2002-10-22

    This paper describes a novel methodology for the identification of mechanical systems and structures from vibration response measurements. It combines prior information, observational data and predictive finite element models to produce configurations and system parameter values that are most consistent with the available data and model. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. The resulting process enables the estimation of distributions of both individual parameters and system-wide states. Attractive features of this approach include its ability to: (1) provide quantitative measures of the uncertainty of a generated estimate; (2) function effectively when exposed to degraded conditions including: noisy data, incomplete data sets and model misspecification; (3) allow alternative estimates to be produced and compared, and (4) incrementally update initial estimates and analysis as more data becomes available. A series of test cases based on a simple fixed-free cantilever beam is presented. These results demonstrate that the algorithm is able to identify the system, based on the stiffness matrix, given applied force and resultant nodal displacements. Moreover, it effectively identifies locations on the beam where damage (represented by a change in elastic modulus) was specified.

  20. Rapid method for sampling metals for materials identification

    Science.gov (United States)

    Higgins, L. E.

    1971-01-01

    Nondamaging process similar to electrochemical machining is useful in obtaining metal samples from places inaccessible to conventional sampling methods or where methods would be hazardous or contaminating to specimens. Process applies to industries where metals or metal alloys play a vital role.

  1. Inertia Force Identification of Cantilever under Moving-Mass by Inverse Method

    Directory of Open Access Journals (Sweden)

    Qiang Chen

    2012-12-01

    Full Text Available In this paper, a recursive inverse method is applied to solve the identification problem of inertia force between the cantilever and moving mass. The recursive inverse method consists of two parts: Kalman filter and recursive least-square algorithm. The basic Euler-Bernoulli beam model is introduced. Then, the differential equations and the state space model of the modal responses and the inertia force can be obtained. Finally, the recursive inverse method, which is based on the discretized state function of the system, is adapted. The identification results show that the recursive inverse method is suitable to be adapted in this problem. Some characteristics of the identification results are discussed and some further conclusions are reached.

  2. Accuracy of conventional identification methods used for Enterobacteriaceae isolates in three Nigerian hospitals

    Science.gov (United States)

    Ogunlowo, Peter Oladejo; Olley, Mitsan; Springer, Burkhard; Allerberger, Franz; Ruppitsch, Werner

    2016-01-01

    Background Enterobacteriaceae are ubiquitously present in nature and can be found in the intestinal tract of humans and animals as commensal flora. Multidrug-resistant Enterobacteriaceae are increasingly reported and are a threat to public health implicating a need for accurate identification of the isolates to species level. In developing countries, identification of bacteria basically depends on conventional methods: culture and phenotypic methods that hamper the accurate identification of bacteria. In this study, matrix-assisted desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technique was compared to conventional identification techniques. Materials and Methods In total, 147 Enterobacteriaceae isolates were collected from March to May 2015 from three medical microbiology laboratories of hospitals in Edo state, Nigeria, after being tested according to the individual laboratories standard operating procedures. All isolates were stored at −20°C until tested centrally by MALDI-TOF MS. Results One hundred and forty five (98.6%) isolates had a MALDI Biotyper best score > or =2.0, indicating a secure genus and probable species identification; and 2(1.36%) isolates had a best score or =2.0 comprised nine genera and 10 species, respectively. A total of 57.2% and 33.1% of isolates identified had agreement between MALDI-TOF MS and conventional techniques for identification at genus and species level, respectively, when analyzing bacteria with MALDI Biotyper best scores > or =2.0. Conclusion The results of our study show that the applied conventional identification techniques for Enterobacteriaceae in the investigated Nigerian hospitals are not very accurate. Use of state-of-the-art identification technologies for microorganisms is necessary to guarantee comparability of bacteriological results. PMID:27703855

  3. Note: Model-based identification method of a cable-driven wearable device for arm rehabilitation

    Science.gov (United States)

    Cui, Xiang; Chen, Weihai; Zhang, Jianbin; Wang, Jianhua

    2015-09-01

    Cable-driven exoskeletons have used active cables to actuate the system and are worn on subjects to provide motion assistance. However, this kind of wearable devices usually contains uncertain kinematic parameters. In this paper, a model-based identification method has been proposed for a cable-driven arm exoskeleton to estimate its uncertainties. The identification method is based on the linearized error model derived from the kinematics of the exoskeleton. Experiment has been conducted to demonstrate the feasibility of the proposed model-based method in practical application.

  4. Method for Face Identification with Facial Action Coding System: FACS Based on Eigen Value Decomposion

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2012-12-01

    Full Text Available Method for face identification based on eigen value decomposition together with tracing trajectories in the eigen space after the eigen value decomposition is proposed. The proposed method allows person to person differences due to faces in the different emotions. By using the well known action unit approach, the proposed method admits the faces in the different emotions. Experimental results show that recognition performance depends on the number of targeted peoples. The face identification rate is 80% for four peoples of targeted number while 100% is achieved for the number of targeted number of peoples is two.

  5. Systems and methods for remote long standoff biometric identification using microwave cardiac signals

    Science.gov (United States)

    McGrath, William R. (Inventor); Talukder, Ashit (Inventor)

    2012-01-01

    Systems and methods for remote, long standoff biometric identification using microwave cardiac signals are provided. In one embodiment, the invention relates to a method for remote biometric identification using microwave cardiac signals, the method including generating and directing first microwave energy in a direction of a person, receiving microwave energy reflected from the person, the reflected microwave energy indicative of cardiac characteristics of the person, segmenting a signal indicative of the reflected microwave energy into a waveform including a plurality of heart beats, identifying patterns in the microwave heart beats waveform, and identifying the person based on the identified patterns and a stored microwave heart beats waveform.

  6. Rapid screening of guar gum using portable Raman spectral identification methods.

    Science.gov (United States)

    Srivastava, Hirsch K; Wolfgang, Steven; Rodriguez, Jason D

    2016-01-25

    Guar gum is a well-known inactive ingredient (excipient) used in a variety of oral pharmaceutical dosage forms as a thickener and stabilizer of suspensions and as a binder of powders. It is also widely used as a food ingredient in which case alternatives with similar properties, including chemically similar gums, are readily available. Recent supply shortages and price fluctuations have caused guar gum to come under increasing scrutiny for possible adulteration by substitution of cheaper alternatives. One way that the U.S. FDA is attempting to screen pharmaceutical ingredients at risk for adulteration or substitution is through field-deployable spectroscopic screening. Here we report a comprehensive approach to evaluate two field-deployable Raman methods--spectral correlation and principal component analysis--to differentiate guar gum from other gums. We report a comparison of the sensitivity of the spectroscopic screening methods with current compendial identification tests. The ability of the spectroscopic methods to perform unambiguous identification of guar gum compared to other gums makes them an enhanced surveillance alternative to the current compendial identification tests, which are largely subjective in nature. Our findings indicate that Raman spectral identification methods perform better than compendial identification methods and are able to distinguish guar gum from other gums with 100% accuracy for samples tested by spectral correlation and principal component analysis.

  7. LocateP : genome-scale subcellular-location predictor for bacterial proteins

    NARCIS (Netherlands)

    Zhou, M.; Boekhorst, J.; Francke, C.; Siezen, R.J.

    2008-01-01

    BACKGROUND: In the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader class

  8. Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.

    Science.gov (United States)

    Flahaut, Nicolas A L; Wiersma, Anne; van de Bunt, Bert; Martens, Dirk E; Schaap, Peter J; Sijtsma, Lolke; Dos Santos, Vitor A Martins; de Vos, Willem M

    2013-10-01

    Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

  9. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

    Directory of Open Access Journals (Sweden)

    Thiele Ines

    2008-09-01

    Full Text Available Abstract Background Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. Results We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate, not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. Conclusion Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i a knowledge-base, ii a discovery tool, and iii an engineering platform to explore P

  10. Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

    Directory of Open Access Journals (Sweden)

    Panda Gurudutta

    2011-05-01

    Full Text Available Abstract Background Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets. Results We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets. Conclusions As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.

  11. Molecular Methods for Identification of Clostridium tetani by Targeting Neurotoxin.

    Science.gov (United States)

    Nagoba, Basavraj; Dharne, Mahesh; Gohil, Kushal N

    2017-01-01

    Tetanus is a potentially fatal muscle spasm disease. It is an important public health problem, especially in rural/tribal areas of developing countries. Tetanus toxin, a neurotoxin (tetanospasmin ), is the most important virulence factor that plays a key role in the pathogenicity of tetanus . Confirmation of virulence by confirming the production of tetanospasmin by infecting species forms the most important part in the diagnosis of tetanus . Various molecular methods have been devised for confirmation of diagnosis by targeting different genes. The most common molecular methods are tetanospasmin producing (TetX) gene-targeted methods using TetX-specific primers. Here, we describe various molecular methods targeting TetX gene such as polymerase chain reaction, pulsed-field gel electrophoresis, Southern blotting, loop-mediated isothermal amplification assay, etc. to confirm the virulence of Cl. tetani.

  12. Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium falciparum

    Science.gov (United States)

    2017-03-22

    Parasitology: Drugs and Drug Resistance journal homepage: www.elsevier .com/locate/ i jpddrUsing a genome-scale metabolic network model to elucidate...the mechanism of chloroquine action in Plasmodium falciparum Shivendra G. Tewari a , *, Sean T. Prigge b, Jaques Reifman a , Anders Wallqvist a , * a ...authors. E-mail addresses: stewari@bhsai.org (S.G. (S.T. Prigge), jaques.reifman.civ@mail.mil (J. Reifma mil ( A . Wallqvist). http://dx.doi.org/10.1016

  13. Molecular Method for Identification of Rickettsia Species in Clinical and Environmental Samples▿

    Science.gov (United States)

    Jado, Isabel; Escudero, Raquel; Gil, Horacio; Jiménez-Alonso, María Isabel; Sousa, Rita; García-Pérez, Ana L.; Rodríguez-Vargas, Manuela; Lobo, Bruno; Anda, Pedro

    2006-01-01

    We present a PCR method targeting the 23S-5S internal transcribed spacer coupled with reverse line blotting that allows Rickettsia species detection and identification in a single step. The method is highly sensitive and specific in identifying Rickettsia species from both patient and environmental samples. The generic approach used allowed us to identify new pathogens. PMID:17035495

  14. A novel identification method of Volterra series in rotor-bearing system for fault diagnosis

    Science.gov (United States)

    Xia, Xin; Zhou, Jianzhong; Xiao, Jian; Xiao, Han

    2016-01-01

    Volterra series is widely employed in the fault diagnosis of rotor-bearing system to prevent dangerous accidents and improve economic efficiency. The identification of the Volterra series involves the infinite-solution problems which is caused by the periodic characteristic of the excitation signal of rotor-bearing system. But this problem has not been considered in the current identification methods of the Volterra series. In this paper, a key kernels-PSO (KK-PSO) method is proposed for Volterra series identification. Instead of identifying the Volterra series directly, the key kernels of Volterra are found out to simply the Volterra model firstly. Then, the Volterra series with the simplest formation is identified by the PSO method. Next, simulation verification is utilized to verify the feasibility and effectiveness of the KK-PSO method by comparison to the least square (LS) method and traditional PSO method. Finally, experimental tests have been done to get the Volterra series of a rotor-bearing test rig in different states, and a fault diagnosis system is built with a neural network to classify different fault conditions by the kernels of the Volterra series. The analysis results indicate that the KK-PSO method performs good capability on the identification of Volterra series of rotor-bearing system, and the proposed method can further improve the accuracy of fault diagnosis.

  15. A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

    Energy Technology Data Exchange (ETDEWEB)

    Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

    2011-07-15

    High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.

  16. Molecular identification of three Indian snake species using simple PCR-RFLP method.

    Science.gov (United States)

    Dubey, Bhawna; Meganathan, P R; Haque, Ikramul

    2010-07-01

    Three endangered Indian snake species, Python molurus, Naja naja, and Xenochrophis piscator are known to be significantly involved in illegal trade. Effective authentication of species is required to curb this illegal trade. In the absence of morphological features, molecular identification techniques hold promise to address the issue of species identification. We present an effective PCR-restriction fragment length polymorphism method for easy identification of the three endangered snake species, Python molurus, Naja naja, and Xenochrophis piscator. A 431-bp amplicon from cytochrome b gene was amplified using novel snake-specific primers following restriction digestion with enzymes Mbo II and Fok I. The species-specific reference fragment patterns were obtained for the target species, which enabled successful identification of even highly degraded shed skin sample confirming the utility of the technique in case of poor-quality DNA. The assay could be effectively used for forensic authentication of three Indian snake species and would help strengthen conservation efforts.

  17. Particle identification method in the CsI(Tl) scintillator used for the CHIMERA 4{pi} detector

    Energy Technology Data Exchange (ETDEWEB)

    Alderighi, M.; Anzalone, A.; Basssini, R.; Berceanu, I.; Blicharska, J.; Boiano, C.; Borderie, B.; Bougault, R.; Bruno, M.; Cali, C.; Cardella, G. E-mail: cardella@ct.infn.it; Cavallaro, Sl.; D' Agostino, M.; D' Andrea, M.; Dayras, R.; De Filippo, E.; Fichera, F.; Geraci, E.; Giustolisi, F.; Grzeszczuk, A.; Guardone, N.; Guazzoni, P.; Guinet, D.; Iacono-Manno, C.M.; Kowalski, S.; La Guidara, E.; Lanchais, A.L.; Lanzalone, G.; Lanzano, G.; Le Neindre, N.; Li, S.; Maiolino, C.; Majka, Z.; Manfredi, G.; Nicotra, D.; Paduszynski, T.; Pagano, A.; Papa, M.; Petrovici, C.M.; Piasecki, E.; Pirrone, S.; Politi, G.; Pop, A.; Porto, F.; Rivet, M.F.; Rosato, E.; Sacca, G.; Sechi, G.; Simion, V.; Sperduto, M.L.; Steckmeyer, J.C.; Trifiro, A.; Trimarchi, M.; Urso, S.; Vannini, G.; Vigilante, M.; Wilczynski, J.; Wu, H.; Xiao, Z.; Zetta, L.; Zipper, W

    2002-08-21

    The charged particle identification obtained by the analysis of signals coming from the CsI(Tl) detectors of the CHIMERA 4{pi} heavy-ion detector is presented. A simple double-gate integration method, with the use of the cyclotron radiofrequency as reference time, results in low thresholds for isotopic particle identification. The dependence of the identification quality on the gate generation timing is discussed. Isotopic identification of light ions up to Beryllium is clearly seen. For the first time also the identification of Z=5 particles is observed. The identification of neutrons interacting with CsI(Tl) by (n,{alpha}) and (n,{gamma}) reactions is also discussed.

  18. Particle identification method in the CsI(Tl) scintillator used for the CHIMERA 4 pi detector

    CERN Document Server

    Alderighi, M; Basssini, R; Berceanu, I; Blicharska, J; Boiano, C; Borderie, B; Bougault, R; Bruno, M; Cali, C; Cardella, G; Cavallaro, S; D'Agostino, M; D'andrea, M; Dayras, R; De Filippo, E; Fichera, F; Geraci, E; Giustolisi, F; Grzeszczuk, A; Guardone, N; Guazzoni, P; Guinet, D; Iacono-Manno, M; Kowalski, S; La Guidara, E; Lanchais, A L; Lanzalone, G; Lanzanò, G; Le Neindre, N; Li, S; Maiolino, C; Majka, Z; Manfredi, G; Nicotra, D; Paduszynski, T; Pagano, A; Papa, M; Petrovici, C M; Piasecki, E; Pirrone, S; Politi, G; Pop, A; Porto, F; Rivet, M F; Rosato, E; Sacca, G; Sechi, G; Simion, V; Sperduto, M L; Steckmeyer, J C; Trifiró, A; Trimarchi, M; Urso, S; Vannini, G; Vigilante, M; Wilczynski, J; Wu, H; Xiao, Z; Zetta, L; Zipper, W

    2002-01-01

    The charged particle identification obtained by the analysis of signals coming from the CsI(Tl) detectors of the CHIMERA 4 pi heavy-ion detector is presented. A simple double-gate integration method, with the use of the cyclotron radiofrequency as reference time, results in low thresholds for isotopic particle identification. The dependence of the identification quality on the gate generation timing is discussed. Isotopic identification of light ions up to Beryllium is clearly seen. For the first time also the identification of Z=5 particles is observed. The identification of neutrons interacting with CsI(Tl) by (n,alpha) and (n,gamma) reactions is also discussed.

  19. Identification of Employee Performance Appraisal Methods in Agricultural Organizations

    Directory of Open Access Journals (Sweden)

    Venclova Katerina

    2013-06-01

    Full Text Available A formal employee performance appraisal is regarded as one of the tools of human resources performance management. People, their knowledge and skills are currently considered to be the most valuable resource a company has. The article focuses on methods of employee performance appraisal in agricultural organizations in the Czech Republic. The first part of the article deals with the theoretical background of the term “formal appraisal” and employee performance appraisal methods as defined by Czech and foreign specialists. Further, the article describes, based on a questionnaire survey, employee performance appraisal methods that are considered importantfor the agricultural organizations in the Czech Republic. The aim of the article is to identify the current state of formal employee appraisal in a sample group of agricultural organizationsand to test dependencies between selected qualitative characteristics. The outcomes show that the most commonly used methods of employee performance appraisal in agricultural organizations include predefined goal-based performance appraisal, predefined standard outcome-based performance appraisal and appraisal interviews. Agricultural organizations apply these methodsin particular due to the fact that their findings are further utilized in other areas of human resource management, such as reward system and personnel planning. In statistical terms, dependencybetween the method of employee performance appraisal according to predefined goals applied by agricultural organizations and personnel planning (an area of human resources managementhas been proven (p-value: 0.032, Phi coefficient: 4.578.

  20. Particle identification using CsI(Tl) crystal with three different methods

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Three pulse-shape-discrimination (PSD) methods are applied to study the particle identification (PID) by using CsI(Tl) crystal, especially for identifying light charged particles. The zero-cross time method, fast and total component method and signal rise time method are used. The experiment, data analysis and results are compared. Good PID for p, α and γ, can be achieved with a CsI(Tl)-photomultiplier assembly.

  1. "GRAY-BOX" MODELING METHOD AND PARAMETERS IDENTIFICATION FOR LARGE-SCALE HYDRAULIC SYSTEM

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Modeling and digital simulation is an effective method to analyze the dynamic characteristics of hydraulic system. It is difficult to determine some performance parameters in the hydraulic system by means of currently used modeling methods. The "gray-box" modeling method for large-scale hydraulic system is introduced. The principle of the method, the submodels of some components and the parameters identification of components or subsystem are researched.

  2. Fatigue crack identification method based on strain amplitude changing

    Science.gov (United States)

    Guo, Tiancai; Gao, Jun; Wang, Yonghong; Xu, Youliang

    2017-09-01

    Aiming at the difficulties in identifying the location and time of crack initiation in the castings of helicopter transmission system during fatigue tests, by introducing the classification diagnostic criteria of similar failure mode to find out the similarity of fatigue crack initiation among castings, an engineering method and quantitative criterion for detecting fatigue cracks based on strain amplitude changing is proposed. This method is applied on the fatigue test of a gearbox housing, whose results indicates: during the fatigue test, the system alarms when SC strain meter reaches the quantitative criterion. The afterwards check shows that a fatigue crack less than 5mm is found at the corresponding location of SC strain meter. The test result proves that the method can provide accurate test data for strength life analysis.

  3. Application of Story-wise Shear Building Identification Method to Actual Ambient Vibration

    Directory of Open Access Journals (Sweden)

    Kohei eFujita

    2015-02-01

    Full Text Available A sophisticated and smart story stiffness System Identification (SI method for a shear building model is applied to a full-scale building frame subjected to micro-tremors. The advantageous and novel feature is that not only the modal parameters, such as natural frequencies and damping ratios, but also the physical model parameters, such as story stiffnesses and damping coefficients, can be identified using micro-tremors. While the building responses to earthquake ground motions are necessary in the previous SI method, it is shown in this paper that the micro-tremor measurements in a full-scale 5 story building frame can be used for identification within the same framework. The SI using micro-tremor measurements leads to the enhanced usability of the previously proposed story-wise shear building identification method. The degree of ARX models and cut-off frequencies of band-pass filter are determined to derive reliable results.

  4. Isolation and Identification of Listeria monocytogenes in Processed Meat by a Combined Cultural-molecular Method

    Directory of Open Access Journals (Sweden)

    Angela Ingianni

    2007-01-01

    Full Text Available The isolation and identification of Listeria monocytogenes in processed meat samples by a combined cultural-molecular method is described. It allows the identification of Listeria strains by means of a hybridization technique with a specific DNA probe directed to the listerial internalin gene. The specificity of this method was found to be 100% and sensitivity was as low as 1 CFU/2.5 g of food sample. A total of 278 meat samples were tested in comparison with PCR and conventional cultural assays. A total of 42 (15.4% L. monocytogenes were detected. PCR analysis gave 3 false negative results and culture failed to detect the Listeria in 5 cases. With this cultural-molecular method the identification and quantitative detection of L. monocytogenes were achieved within 36 hours and no false positive or negative tests were obtained, thus fitting most food industry requirements.

  5. Identification of buried victims in natural disaster with GPR method

    Science.gov (United States)

    Dewi, Rianty Kusuma; Kurniawan, Adityo; Taqwantara, Reyhan Fariz; Iskandar, Farras M.; Naufal, Taufiq Ziyan; Widodo

    2017-07-01

    Indonesian is one of the most seismically active regions in the world and has very complicated plate convergence because there is meeting point of several tectonic plates. The complexity of tectonic features causes a lot of natural disasters such as landslides, tsunamis, earth quakes, volcanoes eruption, etc. Sometimes, the disasters occurs in high populated area and causing thousands to millions of victim been buried under the rumble. Unfortunately, the evacuation still uses the conventional method such using rescue dogs whereas the sensitivity of smell is decrease when the victims buried under the level of the ground. The purpose of this study is to detect buried bodies using GPR method, so it can enhance the effectiveness and the efficiency in looking for the disaster victims. GPR method is used because it can investigate things under the ground. A detailed GPR research has been done in Cikutra Graveyard, Bandung, with corpse buried two week until two years before the research. The radar profiles from this research showed amplitude contras anomaly between the new corpse and the old ones. We obtained the amplitude contras at 1.2-1.4 meters under the surface. This method proved to be effective but still need more attention on undulated surface and non-soil areas.

  6. Benchmarking ortholog identification methods using functional genomics data.

    NARCIS (Netherlands)

    Hulsen, T.; Huynen, M.A.; Vlieg, J. de; Groenen, P.M.

    2006-01-01

    BACKGROUND: The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the defini

  7. Sponge cell culture? A molecular identification method for sponge cells

    NARCIS (Netherlands)

    Sipkema, D.; Heilig, G.H.J.; Akkermans, A.D.L.; Osinga, R.; Tramper, J.; Wijffels, R.H.

    2003-01-01

    Dissociated sponge cells are easily confused with unicellular organisms. This has been an obstacle in the development of sponge-cell lines. We developed a molecular detection method to identify cells of the sponge Dysidea avara in dissociated cell cultures. The 18S ribosomal RNA gene from a Dysidea

  8. Benchmarking ortholog identification methods using functional genomics data.

    NARCIS (Netherlands)

    Hulsen, T.; Huynen, M.A.; Vlieg, J. de; Groenen, P.M.

    2006-01-01

    BACKGROUND: The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the defini

  9. Similar Words Identification Using Naive and TF-IDF Method

    Directory of Open Access Journals (Sweden)

    Divya K.S.

    2014-10-01

    Full Text Available Requirement satisfaction is one of the most important factors to success of software. All the requirements that are specified by the customer should be satisfied in every phase of the development of the software. Satisfaction assessment is the determination of whether each component of the requirement has been addressed in the design document. The objective of this paper is to implement two methods to identify the satisfied requirements in the design document. To identify the satisfied requirements, similar words in both of the documents are determined. The methods such as Naive satisfaction assessment and TF-IDF satisfaction assessment are performed to determine the similar words that are present in the requirements document and design documents. The two methods are evaluated on the basis of the precision and recall value. To perform the stemming, the Porter’s stemming algorithm is used. The satisfaction assessment methods would determine the similarity in the requirement and design documents. The final result would give a accurate picture of the requirement satisfaction so that the defects can be determined at the early stage of software development. Since the defects determines at the early stage, the cost would be low to correct the defects.

  10. A system boundary identification method for life cycle assessment

    DEFF Research Database (Denmark)

    Li, Tao; Zhang, Hongchao; Liu, Zhichao

    2014-01-01

    of processes considered, and the gradient of the fitting curve trends to zero gradually. According to the threshold rules, a relatively accurate system boundary could be obtained.It is found from this research that the system boundary curve describes the growth of life cycle impact assessment (LCIA) results......Life cycle assessment (LCA) is a useful tool for quantifying the overall environmental impacts of a product, process, or service. The scientific scope and boundary definition are important to ensure the accuracy of LCA results. Defining the boundary in LCA is difficult and there are no commonly...... accepted scientific methods yet. The objective of this research is to present a comprehensive discussion of system boundaries in LCA and to develop an appropriate boundary delimitation method.A product system is partitioned into the primary system and interrelated subsystems. The hierarchical relationship...

  11. Comparing Subspace Methods for Closed Loop Subspace System Identification by Monte Carlo Simulations

    Directory of Open Access Journals (Sweden)

    David Di Ruscio

    2009-10-01

    Full Text Available A novel promising bootstrap subspace system identification algorithm for both open and closed loop systems is presented. An outline of the SSARX algorithm by Jansson (2003 is given and a modified SSARX algorithm is presented. Some methods which are consistent for closed loop subspace system identification presented in the literature are discussed and compared to a recently published subspace algorithm which works for both open as well as for closed loop data, i.e., the DSR_e algorithm as well as the bootstrap method. Experimental comparisons are performed by Monte Carlo simulations.

  12. High-precision system identification method for a deformable mirror in wavefront control.

    Science.gov (United States)

    Huang, Lei; Ma, Xingkun; Bian, Qi; Li, Tenghao; Zhou, Chenlu; Gong, Mali

    2015-05-10

    Based on a mathematic model, the relation between the accuracy of the influence matrix and the performance of the wavefront correction is established. Based on the least squares method, a two-step system identification is proposed to improve the accuracy of the influence matrix, where the measurement noise can be suppressed and the nonlinearity of the deformable mirror can be compensated. The validity of the two-step system identification method is tested in the experiment, where improvements in wavefront correction precision as well as closed-loop control efficiency were observed.

  13. An online parameter identification method for time dependent partial differential equations

    Science.gov (United States)

    Boiger, R.; Kaltenbacher, B.

    2016-04-01

    Online parameter identification is of importance, e.g., for model predictive control. Since the parameters have to be identified simultaneously to the process of the modeled system, dynamical update laws are used for state and parameter estimates. Most of the existing methods for infinite dimensional systems either impose strong assumptions on the model or cannot handle partial observations. Therefore we propose and analyse an online parameter identification method that is less restrictive concerning the underlying model and allows for partial observations and noisy data. The performance of our approach is illustrated by some numerical experiments.

  14. Spectral analysis methods for vehicle interior vibro-acoustics identification

    Science.gov (United States)

    Hosseini Fouladi, Mohammad; Nor, Mohd. Jailani Mohd.; Ariffin, Ahmad Kamal

    2009-02-01

    Noise has various effects on comfort, performance and health of human. Sound are analysed by human brain based on the frequencies and amplitudes. In a dynamic system, transmission of sound and vibrations depend on frequency and direction of the input motion and characteristics of the output. It is imperative that automotive manufacturers invest a lot of effort and money to improve and enhance the vibro-acoustics performance of their products. The enhancement effort may be very difficult and time-consuming if one relies only on 'trial and error' method without prior knowledge about the sources itself. Complex noise inside a vehicle cabin originated from various sources and travel through many pathways. First stage of sound quality refinement is to find the source. It is vital for automotive engineers to identify the dominant noise sources such as engine noise, exhaust noise and noise due to vibration transmission inside of vehicle. The purpose of this paper is to find the vibro-acoustical sources of noise in a passenger vehicle compartment. The implementation of spectral analysis method is much faster than the 'trial and error' methods in which, parts should be separated to measure the transfer functions. Also by using spectral analysis method, signals can be recorded in real operational conditions which conduce to more consistent results. A multi-channel analyser is utilised to measure and record the vibro-acoustical signals. Computational algorithms are also employed to identify contribution of various sources towards the measured interior signal. These achievements can be utilised to detect, control and optimise interior noise performance of road transport vehicles.

  15. A graphic method for identification of novel glioma related genes.

    Science.gov (United States)

    Gao, Yu-Fei; Shu, Yang; Yang, Lei; He, Yi-Chun; Li, Li-Peng; Huang, GuaHua; Li, Hai-Peng; Jiang, Yang

    2014-01-01

    Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

  16. A Graphic Method for Identification of Novel Glioma Related Genes

    Directory of Open Access Journals (Sweden)

    Yu-Fei Gao

    2014-01-01

    Full Text Available Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

  17. System identification by methods from the statistical signal theory, history and state of the art

    Energy Technology Data Exchange (ETDEWEB)

    Christensen, Palle [Risoe National Laboratory, Roskilde (Denmark); Gundersen, Vidar B. [Institutt for energiteknikk, OECD Halden Project, Halden (Norway)

    1999-05-15

    Condition monitoring is an important area in which the OECD Halden Reactor Project has developed several tools. This paper presents a general overview of methods utilised in diagnosis systems, signal validation systems and process optimisation systems such as EFD, Mocom, Aladdin and PEANO. An overview of lessons learned on diagnosis of technical systems with special reference to system identification is reported. The analysis of input-output behaviour by special, suitable methods may be used as a tool for diagnosis. An overview of methods for empirical modelling and data analysis and their major differences is presented. It is explained how system identification methods and transforms may be used to build models based on observed data from a system. According to the Webster dictionary diagnosis is 'Investigation or analysis of the cause or nature of a condition, situation or a problem.' By examining data collected from a process the aim is to detect abnormal conditions and if possible understand what has been the cause of the observed problem. Section 1 gives a retrospective view at the development in the field of system identification. Section 2 presents a classification of the methods, while section 3 provides some practical advice on how diagnosis can be carried out by means of system identification methods (author) (ml)

  18. A feature point identification method for positron emission particle tracking with multiple tracers

    Science.gov (United States)

    Wiggins, Cody; Santos, Roque; Ruggles, Arthur

    2017-01-01

    A novel detection algorithm for Positron Emission Particle Tracking (PEPT) with multiple tracers based on optical feature point identification (FPI) methods is presented. This new method, the FPI method, is compared to a previous multiple PEPT method via analyses of experimental and simulated data. The FPI method outperforms the older method in cases of large particle numbers and fine time resolution. Simulated data show the FPI method to be capable of identifying 100 particles at 0.5 mm average spatial error. Detection error is seen to vary with the inverse square root of the number of lines of response (LORs) used for detection and increases as particle separation decreases.

  19. Identification and evaluation of desertification reversal in China:indicators and methods review

    Institute of Scientific and Technical Information of China (English)

    Ning Liu; LiHua Zhou; Yong Chen; Shan Huang

    2014-01-01

    With gradual emergence of desertification reversal after inception of combative attempts in China, its evaluation which is based on solid identification is imperative. However, either identification or evaluation lacks consensus and are limited in earlier studies. This paper endeavors to fill this gap by relying on a systematic review of recent studies. After a clarification of causal relation of identification and evaluation, an argument of indicators and models selection is presented taking sustainability, cost and benefits, public interest and individual behavior into account. At first, we emphasize the logical and sequential importance of identification as prerequisite of evaluation, and demonstrate that fundamental identification could be operated by both direct and indirect implementations, though direct implementation usually seems to be a favorite al-ternative. Here, we emphasize a consideration of sustainable development, cost and benefits, public interest and individual behaviors together, due to the hiatus of incorporating economic and ecological segments together, and the innate theoret-ical deficiencies on environmental products of traditional economic and ecological methods of evaluation. Thus, we conceptually merge the economic and ecological segments into one theoretical framework. However, no matter how elaborate the models is, there is an apparent gap of both identification and evaluation due to failure of dynamic interpre-tations, thereby, it may be future trend and urgency on models’ elaboration.

  20. Photogrammetric Method and Software for Stream Planform Identification

    Science.gov (United States)

    Stonedahl, S. H.; Stonedahl, F.; Lohberg, M. M.; Lusk, K.; Miller, D.

    2013-12-01

    Accurately characterizing the planform of a stream is important for many purposes, including recording measurement and sampling locations, monitoring change due to erosion or volumetric discharge, and spatial modeling of stream processes. While expensive surveying equipment or high resolution aerial photography can be used to obtain planform data, our research focused on developing a close-range photogrammetric method (and accompanying free/open-source software) to serve as a cost-effective alternative. This method involves securing and floating a wooden square frame on the stream surface at several locations, taking photographs from numerous angles at each location, and then post-processing and merging data from these photos using the corners of the square for reference points, unit scale, and perspective correction. For our test field site we chose a ~35m reach along Black Hawk Creek in Sunderbruch Park (Davenport, IA), a small, slow-moving stream with overhanging trees. To quantify error we measured 88 distances between 30 marked control points along the reach. We calculated error by comparing these 'ground truth' distances to the corresponding distances extracted from our photogrammetric method. We placed the square at three locations along our reach and photographed it from multiple angles. The square corners, visible control points, and visible stream outline were hand-marked in these photos using the GIMP (open-source image editor). We wrote an open-source GUI in Java (hosted on GitHub), which allows the user to load marked-up photos, designate square corners and label control points. The GUI also extracts the marked pixel coordinates from the images. We also wrote several scripts (currently in MATLAB) that correct the pixel coordinates for radial distortion using Brown's lens distortion model, correct for perspective by forcing the four square corner pixels to form a parallelogram in 3-space, and rotate the points in order to correctly orient all photos of

  1. Pavement crack identification based on automatic threshold iterative method

    Science.gov (United States)

    Lu, Guofeng; Zhao, Qiancheng; Liao, Jianguo; He, Yongbiao

    2017-01-01

    Crack detection is an important issue in concrete infrastructure. Firstly, the accuracy of crack geometry parameters measurement is directly affected by the extraction accuracy, the same as the accuracy of the detection system. Due to the properties of unpredictability, randomness and irregularity, it is difficult to establish recognition model of crack. Secondly, various image noise, caused by irregular lighting conditions, dark spots, freckles and bump, exerts an influence on the crack detection accuracy. Peak threshold selection method is improved in this paper, and the processing of enhancement, smoothing and denoising is conducted before iterative threshold selection, which can complete the automatic selection of the threshold value in real time and stability.

  2. Emotion identification method using RGB information of human face

    Science.gov (United States)

    Kita, Shinya; Mita, Akira

    2015-03-01

    Recently, the number of single households is drastically increased due to the growth of the aging society and the diversity of lifestyle. Therefore, the evolution of building spaces is demanded. Biofied Building we propose can help to avoid this situation. It helps interaction between the building and residents' conscious and unconscious information using robots. The unconscious information includes emotion, condition, and behavior. One of the important information is thermal comfort. We assume we can estimate it from human face. There are many researchs about face color analysis, but a few of them are conducted in real situations. In other words, the existing methods were not used with disturbance such as room lumps. In this study, Kinect was used with face-tracking. Room lumps and task lumps were used to verify that our method could be applicable to real situation. In this research, two rooms at 22 and 28 degrees C were prepared. We showed that the transition of thermal comfort by changing temperature can be observed from human face. Thus, distinction between the data of 22 and 28 degrees C condition from face color was proved to be possible.

  3. Industrial Process Identification and Control Design Step-test and Relay-experiment-based Methods

    CERN Document Server

    Liu, Tao

    2012-01-01

      Industrial Process Identification and Control Design is devoted to advanced identification and control methods for the operation of continuous-time processes both with and without time delay, in industrial and chemical engineering practice.   The simple and practical step- or relay-feedback test is employed when applying the proposed identification techniques, which are classified in terms of common industrial process type: open-loop stable; integrating; and unstable, respectively. Correspondingly, control system design and tuning models that follow are presented for single-input-single-output processes.   Furthermore, new two-degree-of-freedom control strategies and cascade control system design methods are explored with reference to independently-improving, set-point tracking and load disturbance rejection. Decoupling, multi-loop, and decentralized control techniques for the operation of multiple-input-multiple-output processes are also detailed. Perfect tracking of a desire output trajectory is realiz...

  4. Modal Identification of Offshore Platforms Using Statistical Method Based on ERA

    Institute of Scientific and Technical Information of China (English)

    WANG Shu-qing; LI Hua-jun; T.Takayama

    2005-01-01

    Identification of modal parameters of a linear structure with output-only measurements has received much attention over the past decades. In the paper, the Natural Excitation Technique (NExT) is used for acquisition of the impulse signals from the structural responses. Then Eigensystem Realization Algorithm (ERA) is utilized for modal identification.(SAMFM), is developed to distinguish the true modes from noise modes, and to improve the precision of the identified modal frequencies of the structure. An offshore platform is modeled with the finite element method. The theoretical modal parameters are obtained for a comparison with the identified values. The dynamic responses of the platform under random wave loading are computed for providing the output signals used for identification with ERA. Results of simulation demonstrate that the proposed method can determine the system modal frequency with high precision.

  5. Investigation of lateral-directional aerodynamic parameters identification method for fly-by-wire passenger airliners

    Institute of Scientific and Technical Information of China (English)

    Wu Zhao; Wang Lixin; Lin Jiaming; Ai Junqiang

    2014-01-01

    A new identification method is proposed to solve the problem of the influence on the loaded excitation signals brought by high feedback gain augmentation in lateral-directional aerody-namic parameters identification of fly-by-wire (FBW) passenger airliners. Taking for example an FBW passenger airliner model with directional relaxed-static-stability, through analysis of its signal energy distribution and airframe frequency response, a new method is proposed for signal type selec-tion, signal parameters design, and the appropriate frequency relationship between the aileron and rudder excitation signals. A simulation validation is presented of the FBW passenger airliner’s lat-eral-directional aerodynamic parameters identification. The validation result demonstrates that the designed signal can excite the lateral-directional motion mode of the FBW passenger airliner ade-quately and persistently. Meanwhile, the relative errors of aerodynamic parameters are less than 5%.

  6. Research on palmprint identification method based on quantum algorithms.

    Science.gov (United States)

    Li, Hui; Zhang, Zhanzhan

    2014-01-01

    Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.

  7. Research on Palmprint Identification Method Based on Quantum Algorithms

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-01-01

    Full Text Available Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.

  8. A Method for Identification of Selenoprotein Genes in Archaeal Genomes

    Institute of Scientific and Technical Information of China (English)

    Mingfeng Li; Yanzhao Huang; Yi Xiao

    2009-01-01

    The genetic codon UGA has a dual function: serving as a terminator and encoding selenocysteine. However, most popular gene annotation programs only take it as a stop signal, resulting in misannotation or completely missing selenoprotein genes. We developed a computational method named Asec-Prediction that is specific for the prediction of archaeal selenoprotein genes. To evaluate its effectiveness, we first applied it to 14 archaeal genomes with previously known selenoprotein genes, and Asec-Prediction identified all reported selenoprotein genes without redundant results. When we applied it to 12 archaeal genomes that had not been researched for selenoprotein genes, Asec-Prediction detected a novel selenoprotein gene in Methanosarcina acetivorans. Further evidence was also collected to support that the predicted gene should be a real selenoprotein gene. The result shows that Asec-Prediction is effective for the prediction of archaeal selenoprotein genes.

  9. New methods of microbiological identification using MALDI-TOF

    Directory of Open Access Journals (Sweden)

    Jacyr Pasternak

    2012-03-01

    Full Text Available Rapid diagnosis of pathogens is decisive to guarantee adequatetherapy in infections: culture methods are precise and sensitive, butrather slow. New resources are available to enable faster diagnosis,and the most promising is MALDI-TOF technology: mass spectrometryapplied to microbiological diagnosis. Times as fast as 10 to 15 minutes to etiological diagnosis are possible after a positive blood culture result. We hope to have this technology in our laboratory, ANVISA permitting and improving their very slow rate of doing things… MALDI-TOF is basically putting a sample of culture or an enriched suspension of the probable pathogen over a small spot with a matrix and vaporizing it with a laser pulse: the products are aspired into a chamber, ionized and reach detectors at variable times: the detectors show time of arrival and quantity of the product, and each pathogen has its characteristic spectrum analyzed by a software.

  10. Identification of proteins associated with amyloidosis by polarity index method.

    Science.gov (United States)

    Polanco, Carlos; Samaniego, José Lino; Uversky, Vladimir N; Castañón-González, Jorge Alberto; Buhse, Thomas; Leopold-Sordo, Marili; Madero-Arteaga, Alejandro; Morales-Reyes, Alicia; Tavera-Sierra, Lourdes; González-Bernal, Jesus A; Arias-Estrada, Miguel

    2015-01-01

    There is a natural protein form, insoluble and resistant to proteolysis, adopted by many proteins independently of their amino acid sequences via specific misfolding-aggregation process. This dynamic process occurs in parallel with or as an alternative to physiologic folding, generating toxic protein aggregates that are deposited and accumulated in various organs and tissues. These proteinaceous deposits typically represent bundles of β-sheet-enriched fibrillar species known as the amyloid fibrils that are responsible for serious pathological conditions, including but not limited to neurodegenerative diseases, grouped under the term amyloidoses. The proteins that might adopt this fibrillar conformation are some globular proteins and natively unfolded (or intrinsically disordered) proteins. Our work shows that intrinsically disordered and intrinsically ordered proteins can be reliably identified, discriminated, and differentiated by analyzing their polarity profiles generated using a computational tool known as the polarity index method (Polanco & Samaniego, 2009; Polanco et al., 2012; 2013; 2013a; 2014; 2014a; 2014b; 2014c; 2014d). We also show that proteins expressed in neurons can be differentiated from proteins in these two groups based on their polarity profiles, and also that this computational tool can be used to identify proteins associated with amyloidoses. The efficiency of the proposed method is high (i.e. 70%) as evidenced by the analysis of peptides and proteins in the APD2 database (2012), AVPpred database (2013), and CPPsite database (2013), the set of selective antibacterial peptides from del Rio et al. (2001), the sets of natively unfolded and natively folded proteins from Oldfield et al. (2005), the set of human revised proteins expressed in neurons, and non-human revised proteins expressed in neurons, from the Uniprot database (2014), and also the set of amyloidogenic proteins from the AmyPDB database (2014).

  11. A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

    Science.gov (United States)

    Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing

    2017-01-01

    Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405

  12. A simplified fractional order impedance model and parameter identification method for lithium-ion batteries.

    Science.gov (United States)

    Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing

    2017-01-01

    Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.

  13. Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Man Zhu

    2017-03-01

    Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

  14. Isolation and identification methods of Rothia species in oral cavities.

    Science.gov (United States)

    Tsuzukibashi, Osamu; Uchibori, Satoshi; Kobayashi, Taira; Umezawa, Koji; Mashimo, Chiho; Nambu, Takayuki; Saito, Masanori; Hashizume-Takizawa, Tomomi; Ochiai, Tomoko

    2017-03-01

    Rothia dentocariosa and Rothia mucilaginosa which are Gram-positive bacteria are part of the normal flora in the human oral cavity and pharynx. Furthermore, Rothia aeria, which was first isolated from air samples in the Russian space station Mir, is predicted to be an oral inhabitant. Immunocompromised patients are often infected by these organisms, leading to various systemic diseases. The involvement of these organisms in oral infections has attracted little attention, and their distribution in the oral cavity has not been fully clarified because of difficulties in accurately identifying these organisms. A suitable selective medium for oral Rothia species, including R. aeria, is necessary to assess the veritable prevalence of these organisms in the oral cavity. To examine the bacterial population in the oral cavity, a novel selective medium (ORSM) was developed for isolating oral Rothia species in this study. ORSM consists of tryptone, sodium gluconate, Lab-Lemco powder, sodium fluoride, neutral acriflavin, lincomycin, colistin, and agar. The average growth recovery of oral Rothia species on ORSM was 96.7% compared with that on BHI-Y agar. Growth of other representative oral bacteria, i.e. genera Streptococcus, Actinomyces, Neisseria, and Corynebacterium, was remarkably inhibited on the selective medium. PCR primers were designed based on partial sequences of the 16S rDNA genes of oral Rothia species. These primers reacted to each organism and did not react to other non-oral Rothia species or representative oral bacteria. These results indicated that these primers are useful for identifying oral Rothia species. A simple multiplex PCR procedure using these primers was a reliable method of identifying oral Rothia species. The proportion of oral Rothia species in saliva samples collected from 20 subjects was examined by culture method using ORSM. Rothia dentocariosa, Rothia mucilaginosa, and R. aeria accounted for 1.3%, 5.9%, and 0.8% of the total cultivable

  15. A simple method for identification of irradiated spices

    Energy Technology Data Exchange (ETDEWEB)

    Behere, A.; Desai, S.R.P.; Nair, P.M. (Bhabha Atomic Research Centre, Bombay (India). Food Technology and Enzyme Engineering Div.); Rao, S.M.D. (Bhabha Atomic Research Centre, Bombay (India). Technical Physics and Prototype Engineering Div.)

    1992-07-01

    Thermoluminescence (TL) properties of curry powder, a salt containing spice mixture, and three different ground spices, viz, chilli, turmeric and pepper, were compared with TL of table salt. The spices other than curry powder, did not exhibit characteristic TL in the absence of salt. Therefore studies were initiated to develop a simple and reliable method using common salt for distinguishing irradiated spices (10 kGy) from unirradiated ones under normal conditions of storage. Common salt exhibited a characteristic TL glow at 170{sup o}C. However, when present in curry powder, the TL glow of salt showed a shift to 208{sup o}C. It was further observed that upon storage up to 6 months, the TL of irradiated curry powder retained about 10% of the original intensity and still could be distinguished from the untreated samples. From our results it is evident that common salt could be used as an indicator either internally or externally in small sachets for incorporating into prepacked spices. (author).

  16. Identification of rural landscape classes through a GIS clustering method

    Directory of Open Access Journals (Sweden)

    Irene Diti

    2013-09-01

    Full Text Available The paper presents a methodology aimed at supporting the rural planning process. The analysis of the state of the art of local and regional policies focused on rural and suburban areas, and the study of the scientific literature in the field of spatial analysis methodologies, have allowed the definition of the basic concept of the research. The proposed method, developed in a GIS, is based on spatial metrics selected and defined to cover various agricultural, environmental, and socio-economic components. The specific goal of the proposed methodology is to identify homogeneous extra-urban areas through their objective characterization at different scales. Once areas with intermediate urban-rural characters have been identified, the analysis is then focused on the more detailed definition of periurban agricultural areas. The synthesis of the results of the analysis of the various landscape components is achieved through an original interpretative key which aims to quantify the potential impacts of rural areas on the urban system. This paper presents the general framework of the methodology and some of the main results of its first implementation through an Italian case study.

  17. Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification (SSI-COV) method

    Institute of Scientific and Technical Information of China (English)

    Yong Xie; Pan Liu; Guo-Ping Cai

    2016-01-01

    In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the cou-pled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochas-tic subspace identification (SSI-COV) algorithm is adopted to identify the modal parameters of the system. In this algo-rithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decom-position on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally, numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indi-cate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.

  18. Implementation of Steganographic Method Based on IPv4 Identification Field over NS-3

    Directory of Open Access Journals (Sweden)

    Hamza Kheddar

    2015-03-01

    Full Text Available In this paper we present first a study of covert channels (steganography that may be applied for each TCP/IP layer in VoIP application. Then, we present a steganographic method which hide secret data in IP protocol header fields, particularly the identification field. The IP protocol covert channel implementation was carried out in NS-3 (Network Simulator 3.

  19. Combining knowledge- and data-driven methods for de-identification of clinical narratives.

    Science.gov (United States)

    Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George; Keane, John A; Nenadic, Goran

    2015-12-01

    A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities. In addition, we have devised a two-pass recognition approach that creates a patient-specific run-time dictionary from the PHI entities identified in the first step with high confidence, which is then used in the second pass to identify mentions that lack specific clues. The proposed method achieved the overall micro F1-measures of 91% on strict and 95% on token-level evaluation on the test dataset (514 narratives). Whilst most PHI entities can be reliably identified, particularly challenging were mentions of Organizations and Professions. Still, the overall results suggest that automated text mining methods can be used to reliably process clinical notes to identify personal information and thus providing a crucial step in large-scale de-identification of unstructured data for further clinical and epidemiological studies.

  20. Communication with your packaging: possibilities for intelligent functions and identification methods in packaging

    NARCIS (Netherlands)

    Schilthuizen, S.F.

    1999-01-01

    Intelligent functions in packaging can have all kinds of forms. This paper focuses on methods of identifying a packaging and other important tasks which can be performed after identification: tracing, tracking, monitoring and sensing of conditions. The recent developments in ID-technology and sensor

  1. [Methods of identification and assessment of safety of genetically modified microorganisms in manufacture food production].

    Science.gov (United States)

    Khovaev, A A; Nesterenko, L N; Naroditskiĭ, B S

    2011-01-01

    Methods of identification of genetically modified microorganisms (GMM), used in manufacture food on control probes are presented. Results of microbiological and molecular and genetic analyses of food products and their components important in microbiological and genetic expert examination of GMM in foods are considered. Examination of biosafety of GMM are indicated.

  2. Identification of the corporate values of an enterprise: theory, approaches, methodic

    Directory of Open Access Journals (Sweden)

    Kozlov Vladimir Aleksandrovich

    2015-07-01

    Full Text Available This article focuses on research and composition of the instrument to identify the corporate values of the enterprise and practical activities to support them. The first part based on theoretical evidence of essence and correlation between notions of ‘Corporate culture’ and ‘Corporate values’. The method of corporate values identification, based on indirect approach, is proposed and the examples are provided. The results of implemented project for corporate values identification at industrial enterprise are presented. For further support the tasks and activities for corporate values management are proposed.

  3. Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains

    CERN Document Server

    Billings, Stephen A

    2013-01-01

    Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) modelThe orthogonal least squares algorithm that allows models to be built term by

  4. Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method

    Energy Technology Data Exchange (ETDEWEB)

    Andrei, Petru [Electrical and Computer Engineering Department, Florida State Unviersity, Tallahassee, FL 32310 (United States) and Electrical and Computer Engineering Department, Florida A and M Unviersity, Tallahassee, FL 32310 (United States)]. E-mail: pandrei@eng.fsu.edu; Oniciuc, Liviu [Electrical and Computer Engineering Department, Florida State Unviersity, Tallahassee, FL 32310 (United States); Stancu, Alexandru [Faculty of Physics, ' Al. I. Cuza' University, Iasi 700506 (Romania); Stoleriu, Laurentiu [Faculty of Physics, ' Al. I. Cuza' University, Iasi 700506 (Romania)

    2007-09-15

    An identification technique for the parameters of phenomenological models of hysteresis is presented. The basic idea of our technique is to set up a system of equations for the parameters of the model as a function of known quantities on the major or minor hysteresis loops (e.g. coercive force, susceptibilities at various points, remanence), or other magnetization curves. This system of equations can be either over or underspecified and is solved by using the conjugate gradient method. Numerical results related to the identification of parameters in the Energetic, Jiles-Atherton, and Preisach models are presented.

  5. A rapid (less than 10 minute) electrophoresis method for identification of wheat varieties.

    Science.gov (United States)

    Wrigley, C W; Gore, P J; Manusu, H P

    1991-05-01

    Conventional procedures for electrophoretic identification of grain samples according to variety are too slow to permit checking at the time of delivery. The method described permits electrophoretic identification within an hour. It involves extraction of gliadin proteins from crushed grain with 6% urea solution or ethylene glycol, cathodic electrophoresis for 9 min at 300 V in a Micrograd gel (MG 315 from Gradipore Ltd, Sydney, Australia) using sodium lactate buffer (pH 3.1), and staining in Gradipore (at about 50 degrees C). Distinction between a set of Australian varieties was similar to that obtainable with the Australian Standard Procedure.

  6. New logarithmic technique of diffusivity identification using the flash method; Nouvelle technique logarithmique d`identification de la diffusivite par la methode flash

    Energy Technology Data Exchange (ETDEWEB)

    Thermitus, M.A.; Laurent, M. [Institut National des Sciences Appliquees (INSA), 69 - Villeurbanne (France)

    1997-12-31

    Using a logarithmic transformation, the thermogram of a flash experiment can be interpreted as the sum of the adiabatic model solution with a term representative of the losses. Two methods based on this transformation are proposed in this study. They are based on the identification of a parameter that depends on the thickness of the sample and on its diffusivity and not on the experimental conditions. They allow to identify the diffusivity with a high precision even for materials with a low conductivity at high temperatures. (J.S.) 12 refs.

  7. [Identification of transmission fluid based on NIR spectroscopy by combining sparse representation method with manifold learning].

    Science.gov (United States)

    Jiang, Lu-Lu; Luo, Mei-Fu; Zhang, Yu; Yu, Xin-Jie; Kong, Wen-Wen; Liu, Fei

    2014-01-01

    An identification method based on sparse representation (SR) combined with autoencoder network (AN) manifold learning was proposed for discriminating the varieties of transmission fluid by using near infrared (NIR) spectroscopy technology. NIR transmittance spectra from 600 to 1 800 nm were collected from 300 transmission fluid samples of five varieties (each variety consists of 60 samples). For each variety, 30 samples were randomly selected as training set (totally 150 samples), and the rest 30 ones as testing set (totally 150 samples). Autoencoder network manifold learning was applied to obtain the characteristic information in the 600-1800 nm spectra and the number of characteristics was reduced to 10. Principal component analysis (PCA) was applied to extract several relevant variables to represent the useful information of spectral variables. All of the training samples made up a data dictionary of the sparse representation (SR). Then the transmission fluid variety identification problem was reduced to the problem as how to represent the testing samples from the data dictionary (training samples data). The identification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with that of linear discriminant analysis (LDA), least squares support vector machine (LS-SVM) and sparse representation (SR) using the relevant variables selected by principal component analysis (PCA) and AN. Experimental results demonstrated that the overall identification accuracy of the proposed method for the five transmission fluid varieties was 97.33% by AN-SR, which was significantly higher than that of LDA or LS-SVM. Therefore, the proposed method can provide a new effective method for identification of transmission fluid variety.

  8. Review on applied foods and analyzed methods in identification testing of irradiated foods

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kwang Hoon; Lee, Hoo Chul; Park, Sung Hyun; Kim, Soo Jin; Kim, Kwan Soo [Greenpia Technology Inc., Yeojoo (Korea, Republic of); Jeong, Il Yun; Lee, Ju Woon [Korea Atomic Energy Research Institute, Jeongeup (Korea, Republic of); Yook, Hong Sun [Chungnam National University, Daejeon (Korea, Republic of)

    2010-06-15

    Identification methods of irradiated foods have been adopted as official test by EU and Codex. PSL, TL, ESR and GC/MS methods were registered in Korea food code on 2009 and put in force as control system of verification for labelling of food irradiation. But most generally applicable PSL and TL methods are specified applicable foods according to domestic approved items. Unlike these specifications, foods unpermitted in Korea are included in applicable items of ESR and GC/MS methods. According to recent research data, numerous food groups are possible to effective legal control by identification and these items are demanded to permit regulations for irradiation additionally. Especially, the prohibition of irradiation for meats or seafoods is not harmonized with international standards and interacts as trade friction or industrial restrictions due to unprepared domestic regulation. Hence, extension of domestic legal permission for food irradiation can contrive to related industrial development and also can reduce trade friction and enhance international competitiveness.

  9. Homotopy Iteration Algorithm for Crack Parameters Identification with Composite Element Method

    Directory of Open Access Journals (Sweden)

    Ling Huang

    2013-01-01

    Full Text Available An approach based on homotopy iteration algorithm is proposed to identify the crack parameters in beam structures. In the forward problem, a fully open crack model with the composite element method is employed for the vibration analysis. The dynamic responses of the cracked beam in time domain are obtained from the Newmark direct integration method. In the inverse analysis, an identification approach based on homotopy iteration algorithm is studied to identify the location and the depth of a cracked beam. The identification equation is derived by minimizing the error between the calculated acceleration response and the simulated measured one. Newton iterative method with the homotopy equation is employed to track the correct path and improve the convergence of the crack parameters. Two numerical examples are conducted to illustrate the correctness and efficiency of the proposed method. And the effects of the influencing parameters, such as measurement time duration, measurement points, division of the homotopy parameter and measurement noise, are studied.

  10. New and efficient method using Saccharomyces cerevisiae mutants for identification of siderophores produced by microorganisms.

    Science.gov (United States)

    Park, Yong-Sung; Kim, Ji-Hyun; Chang, Hyo-Ihl; Kim, Seung-Wook; Paik, Hyun-Dong; Kang, Chang-Won; Kim, Tae-Hyoung; Sung, Ha-Chin; Yun, Cheol-Won

    2007-09-01

    The separation and identification of siderophores produced by microorganisms is a time-consuming and an expensive procedure. We have developed a new and efficient method to identify siderophores using well-established Saccharomyces cerevisiae deletion mutants. The Deltafet3,arn strains fail to sustain growth, even when specific siderophores are supplied, and mutants are siderophore-specific: Deltafet3,arn2 for triacetylfusarinine C (TAFC), Deltafet3,arn1,sit1 for ferrichrome (FC), and Deltafet3,sit1 for ferrioxamine B (FOB). The culture broth of Fusarium graminearum was separated by HPLC, and each peak was subjected to a plate assay using S. cerevisiae mutants. We have found that each peak contained specific siderophores produced by F. graminearum, and these coincided with reference siderophores. This method is quite novel because nobody tried this method to identify the siderophores. Furthermore, this method will save time and cost in the identification of siderophores produced by microorganisms.

  11. Fault Identification Method of Ball Bearing Based on IAs and SVMs

    Directory of Open Access Journals (Sweden)

    Chen Chao

    2016-01-01

    Full Text Available In order to effectively identify the bearing running condition, this paper proposed a new method which combines local mean decomposition (LMD and support vector machine (SVM together for ball bearing fault identification. Firstly, the gathered vibration signals were decomposed into a number of product functions (PFs by LMD, with each PF corresponding to an instantaneous amplitude (IA signal and instantaneous frequency (IF signal. Then, introduce the concept of fault characteristic amplitude ratios which can be used to construct fault feature vectors; the extracted characteristic features were input into SVM to train and construct the fault identification model; the bearing running state identification was thereby realized. Cases of normal and fault were analyzed. Experimental results show that the proposed algorithm can diagnose the bearing failures reasonable and efficient.

  12. Optimized genotyping method for identification of bacterial contaminants in pharmaceutical industry.

    Science.gov (United States)

    Stamatoski, Borche; Ilievska, Miroslava; Babunovska, Hristina; Sekulovski, Nikola; Panov, Sasho

    2016-06-01

    Microbiological control is of crucial importance in the pharmaceutical industry regarding the possible bacterial contamination of the environment, water, raw materials and finished products. Molecular identification of bacterial contaminants based on DNA sequencing of the hypervariable 16SrRNA gene has been introduced recently. The aim of this study is to investigate the suitability of gene sequencing using our selection of PCR primers and conditions for rapid and accurate bacterial identification in pharmaceutical industry quality control. DNA was extracted from overnight incubated colonies from 10 bacterial ATCC strains, which are common contaminants in the pharmaceutical industry. A region of bacterial 16SrRNA gene was analyzed by bidirectional DNA sequencing. Bacterial identification based on partial sequencing of the 16SrRNA gene is the appropriate method that could be used in the pharmaceutical industry after adequate validations. We have successfully identified all tested bacteria with more than 99 % similarity to the already published sequences.

  13. Genome-scale screen for DNA methylation-based detection markers for ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Mihaela Campan

    Full Text Available BACKGROUND: The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer. METHODOLOGY/PRINCIPAL FINDINGS: We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels. We identified one marker, IFFO1 promoter methylation (IFFO1-M, that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients. CONCLUSIONS/SIGNIFICANCE: We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers.

  14. Parameter identification theory of a complex model based on global optimization method

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.

  15. Oral candidiasis: a comparison between conventional methods and multiplex polymerase chain reaction for species identification.

    Science.gov (United States)

    Liguori, G; Di Onofrio, V; Lucariello, A; Gallé, F; Signoriello, G; Colella, G; D'Amora, M; Rossano, F

    2009-02-01

    Oral candidiasis is the most common fungal infection in dental practice, and is caused by yeasts that are normally present in the endogenous flora. To evaluate a rapid diagnostic method for identification of Candida oral isolates, a multiplex polymerase chain reaction (PCR) was carried out on colonies and on oral rinse solutions from 95 subjects with suspected oral candidiasis and results were compared with those from seven commonly used phenotypic identification systems. Between four and nine species were characterized in the samples by the phenotypic methods. PCR identified the same species in 60 (74%) samples from both colony and oral rinse solutions. Statistical analysis, carried out only for the three most frequently isolated species (Candida albicans, Candida glabrata, and Candida tropicalis), showed good concordance in the comparison of multiplex PCR with API 20C AUX and with the Rapid Yeast Identification Panel; conversely, significant differences were registered in the comparison between the molecular method and other phenotypic systems, including four chromogenic media and the automated system Vitek2. Multiplex PCR was rapid and effective in the identification of Candida species and allowed the detection of more than one species in the same sample.

  16. Method of and system for identification or estimation of a refractive index of a liquid

    DEFF Research Database (Denmark)

    2015-01-01

    This invention relates to a method of and a system (100) for identification or estimation of a refractive index of a liquid (120) comprising a light receiving part (111) adapted to receive polarised or non-polarised light (125; 135), a light emitting part (112) adapted, during use, to transmit li....../or captured by an image capturing unit (501), enables identification or estimation of the predetermined refractive index of the liquid (120). In this way, a method and a system for identification or estimation of a refractive index of a liquid is readily provided.......This invention relates to a method of and a system (100) for identification or estimation of a refractive index of a liquid (120) comprising a light receiving part (111) adapted to receive polarised or non-polarised light (125; 135), a light emitting part (112) adapted, during use, to transmit...... light (130), an optical structure (110) being adapted to receive, during use, polarised light (125) via or from the light receiving part (111), and being adapted to receive, during use, a liquid (120) having a predetermined refractive index to be identified or estimated, and a first polariser (115...

  17. Rapid method for identification of group B streptococci in neonatal blood cultures.

    OpenAIRE

    Holmes, R. L.; Harada, W A

    1981-01-01

    A rapid technique used for the identification of Streptococcus agalactiae, Lancefield group B, from the blood cultures of two neonatal infants is reported. The method utilized the Phadebact Streptococcus Test System (Pharmacia Diagnostics, Piscataway, N.J.) and the supernatant from 13- and 14-h blood cultures. Additional studies with simulated neonatal blood cultures revealed that this method was reproducible. Additional studies also revealed that some non-specific agglutination did occur, wh...

  18. The Identification of Weak Signals and Wild Cards in Foresight Methodology: Stages and Methods

    OpenAIRE

    Julia V. Ponomareva; Sokolova, Anna V.

    2015-01-01

    This research explores the key stages and methods for the identification of weak signals (WS) and wild cards (WC) in foresight methodology. Theoretical approaches and practical projects in this field were studied, key characteristics and features of these concepts were identified. A review of potential data sources for the monitoring of WS and WC were also provided. The key groups of methods were formed including scanning and monitoring; data analysis; modelling, clustering, interpretation; e...

  19. Nonlinear parameter identification: Ballistic range experience applicable to flight testing. [using Gauss-Newton method

    Science.gov (United States)

    Chapman, G.; Kirk, D.

    1974-01-01

    The parameter identification scheme being used is a differential correction least squares procedure (Gauss-Newton method). The position, orientation, and derivatives of these quantities with respect to the parameters of interest (i.e., sensitivity coefficients) are determined by digital integration of the equations of motion and the parametric differential equations. The application of this technique to three vastly different sets of data is used to illustrate the versatility of the method and to indicate some of the problems that still remain.

  20. An Identification Method of Magnetizing Inrush Current Phenomena in Distribution System

    Science.gov (United States)

    Dou, Naoki; Toyama, Atushi; Satoh, Kohki; Naitoh, Tadashi; Masaki, Kazuyuki

    In high voltage distribution systems, there are many power quality troubles due to voltage dips. Otherwise, a magnetizing inrush current causes the voltage dip. To suppress voltage dips, it is necessary to identify the magnetizing inrush current phenomena. In this paper, the authors propose a new identification method. The principles are that the saturation start/end flux is equal and the inrush current pattern exists. And to avoid a interfere with saturation area overlap; the rectangular coordinate method is adopted.

  1. A review of output-only structural mode identification literature employing blind source separation methods

    Science.gov (United States)

    Sadhu, A.; Narasimhan, S.; Antoni, J.

    2017-09-01

    Output-only modal identification has seen significant activity in recent years, especially in large-scale structures where controlled input force generation is often difficult to achieve. This has led to the development of new system identification methods which do not require controlled input. They often work satisfactorily if they satisfy some general assumptions - not overly restrictive - regarding the stochasticity of the input. Hundreds of papers covering a wide range of applications appear every year related to the extraction of modal properties from output measurement data in more than two dozen mechanical, aerospace and civil engineering journals. In little more than a decade, concepts of blind source separation (BSS) from the field of acoustic signal processing have been adopted by several researchers and shown that they can be attractive tools to undertake output-only modal identification. Originally intended to separate distinct audio sources from a mixture of recordings, mathematical equivalence to problems in linear structural dynamics have since been firmly established. This has enabled many of the developments in the field of BSS to be modified and applied to output-only modal identification problems. This paper reviews over hundred articles related to the application of BSS and their variants to output-only modal identification. The main contribution of the paper is to present a literature review of the papers which have appeared on the subject. While a brief treatment of the basic ideas are presented where relevant, a comprehensive and critical explanation of their contents is not attempted. Specific issues related to output-only modal identification and the relative advantages and limitations of BSS methods both from theoretical and application standpoints are discussed. Gap areas requiring additional work are also summarized and the paper concludes with possible future trends in this area.

  2. A stochastic method for convective storm identification,tracking and nowcasting

    Institute of Scientific and Technical Information of China (English)

    Lei Han; Shengxue Fu; Guang Yang; Hongqing Wang; Yongguang Zheng; Yingjing Lin

    2008-01-01

    The convective storm identification,tracking and nowcasting method is one of the important nowcasting methodologies against severe convective weather.In severe convective cases,such as storm shape or rapid velocity changes,existing methods are apt to provide unsatisfied storm identification,tracking and nowcasting results.To overcome these difficulties,this paper proposes a novel approach to identify,track and short-term forecast (nowcast) of convective storms.A mathematical morphology-based storm identification method is adopted which can identify storm cells accurately in a cluster of storms.As for the difficult tracking problem,sequential Monte Carlo (SMC) method is utilized to simplify the tracking process.It is not only inherently suitable for handling complicated splits and mergers,but also capable of handling the case of storm-missing detection.In order to provide more accurate forecast of a storm position,this method takes the advantages of the cross-correlation method.The qualitative and quantitative evaluations show the efficiency and robustness of the proposed approach.

  3. Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents

    Directory of Open Access Journals (Sweden)

    Ferrández Oscar

    2012-07-01

    Full Text Available Abstract Background The increased use and adoption of Electronic Health Records (EHR causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI, which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act “Safe Harbor” method. This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. Methods We installed and evaluated five text de-identification systems “out-of-the-box” using a corpus of VHA clinical documents. The systems based on machine learning methods were trained with the 2006 i2b2 de-identification corpora and evaluated with our VHA corpus, and also evaluated with a ten-fold cross-validation experiment using our VHA corpus. We counted exact, partial, and fully contained matches with reference annotations, considering each PHI type separately, or only one unique ‘PHI’ category. Performance of the systems was assessed using recall (equivalent to sensitivity and precision (equivalent to positive predictive value metrics, as well as the F2-measure. Results Overall, systems based on rules and pattern matching achieved better recall, and precision was always better with systems based on machine learning approaches. The highest “out-of-the-box” F2-measure was 67% for partial matches; the best precision and recall were 95% and 78%, respectively. Finally, the ten-fold cross validation experiment allowed for an increase of the F2-measure to 79% with partial matches

  4. Use of molecular methods in identification of Candida Species and evaluation of fluconazole resistance

    Directory of Open Access Journals (Sweden)

    Meltem Yalinay Cirak

    2003-12-01

    Full Text Available The aim of this study was to evaluate the use of one of the molecular typing methods such as PCR (polymerase chain reaction following by RFLP (restriction fragment length polymorphism analysis in the identification of Candida species and then to differentiate the identified azole susceptible and resistant Candida albicans strains by using AP-PCR (arbitrarily primed-polymerase chain reaction. The identification of Candida species by PCR and RFLP analysis was based on the size and primary structural variation of rDNA intergenic spacer regions (ITS. Forty-four clinical Candida isolates comprising 5 species were included to the study. The amplification products were digested individually with 3 different restriction enzymes: HaeIII, DdeI, and BfaI. All the isolates tested yielded the expected band patterns by PCR and RFLP analysis. The results obtained from this study demonstrate that Candida species can be differentiated as C. albicans and non-C. albicans strains only by using HaeIII restriction enzyme and BfaI maintains the differentiation of these non-C. albicans species. After identification Candida species with RFLP analysis, C. albicans strains were included to the AP-PCR test. By using AP-PCR, fluconazole susceptible and resistant strains were differentiated. Nine fluconazole susceptible and 24 fluconazole resistant C. albicans were included to the study. Fluconazole resistant strains had more bands when evaluating with the agarose gel electrophoresis but there were no specific discriminatory band patterns to warrant the differentiation of the resistance. The identification of Candida species with the amplification of intergenic spacer region and RFLP analysis is a practical, short, and a reliable method when comparing to the conventional time-consuming Candida species identification methods. The fluconazole susceptibility testing with AP-PCR seems to be a promising method but further studies must be performed for more specific results.

  5. Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production.

    Science.gov (United States)

    Khannapho, Chiraphan; Zhao, Hongjuan; Bonde, Bhushan K; Kierzek, Andrzej M; Avignone-Rossa, Claudio A; Bushell, Michael E

    2008-09-01

    Using flux variability analysis of a genome scale metabolic network of Streptomyces coelicolor, a series of reactions were identified, from disparate pathways that could be combined into an actinorhodin-generating mini-network. Candidate process feed nutrients that might be expected to influence this network were used in process simulations and in silico predictions compared to experimental findings. Ranking potential process feeds by flux balance analysis optimisation, using either growth or antibiotic production as objective function, did not correlate with experimental actinorhodin yields in fed processes. However, the effect of the feeds on glucose assimilation rate (using glucose uptake as objective function) ranked them in the same order as in vivo antibiotic production efficiency, consistent with results of a robustness analysis of the effect of glucose assimilation on actinorhodin production.

  6. IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models.

    Science.gov (United States)

    Ye, Chao; Xu, Nan; Dong, Chuan; Ye, Yuannong; Zou, Xuan; Chen, Xiulai; Guo, Fengbiao; Liu, Liming

    2017-04-07

    Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

  7. Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

    Directory of Open Access Journals (Sweden)

    Ed Reznik

    2017-09-01

    Full Text Available Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.

  8. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity.

    Science.gov (United States)

    Bosi, Emanuele; Monk, Jonathan M; Aziz, Ramy K; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø

    2016-06-28

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

  9. A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

    Science.gov (United States)

    Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

    2016-01-01

    Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. © 2015 The Authors.The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

  10. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

    Science.gov (United States)

    Bosi, Emanuele; Monk, Jonathan M.; Aziz, Ramy K.; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø.

    2016-01-01

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world. PMID:27286824

  11. Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III-reducer Rhodoferax ferrireducens

    Directory of Open Access Journals (Sweden)

    Daugherty Sean

    2009-09-01

    Full Text Available Abstract Background Rhodoferax ferrireducens is a metabolically versatile, Fe(III-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies. Results The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress. Conclusion This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms.

  12. Seed oil polyphenols: rapid and sensitive extraction method and high resolution-mass spectrometry identification.

    Science.gov (United States)

    Koubaa, Mohamed; Mhemdi, Houcine; Vorobiev, Eugène

    2015-05-01

    Phenolic content is a primary parameter for vegetables oil quality evaluation, and directly involved in the prevention of oxidation and oil preservation. Several methods have been reported in the literature for polyphenols extraction from seed oil but the approaches commonly used remain manually handled. In this work, we propose a rapid and sensitive method for seed oil polyphenols extraction and identification. For this purpose, polyphenols were extracted from Opuntia stricta Haw seed oil, using high frequency agitation, separated, and then identified using a liquid chromatography-high resolution mass spectrometry method. Our results showed good sensitivity and reproducibility of the developed methods.

  13. An Improved Identification Method of Magnetizing Inrush Current Phenomena in Distribution System

    Science.gov (United States)

    Maeda, Tatsuhiko; Naitoh, Tadashi; Toyama, Atsushi; Takeda, Keiki

    In this paper, the authors propose an improved identification method of the magnetizing inrush current phenomena. A first improvement is the development of determinant method about the transformer connection, which gives a calculation method of magnetic flux, using with theoretical pattern of inrush current ratio. And then, since the Aitken's Δ2-process, which uses the estimation of the saturation on/off time, has many extrema, it is difficult to determinate the saturation on/off time. Therefore, using the pattern of derivative current, a new determinant method of the saturation on/off time is developed.

  14. Advances in Gas Chromatographic Methods for the Identification of Biomarkers in Cancer

    Directory of Open Access Journals (Sweden)

    Konstantinos A. Kouremenos, Mikael Johansson, Philip J. Marriott

    2012-01-01

    Full Text Available Screening complex biological specimens such as exhaled air, tissue, blood and urine to identify biomarkers in different forms of cancer has become increasingly popular over the last decade, mainly due to new instruments and improved bioinformatics. However, despite some progress, the identification of biomarkers has shown to be a difficult task with few new biomarkers (excluding recent genetic markers being considered for introduction to clinical analysis. This review describes recent advances in gas chromatographic methods for the identification of biomarkers in the detection, diagnosis and treatment of cancer. It presents a general overview of cancer metabolism, the current biomarkers used for cancer diagnosis and treatment, a background to metabolic changes in tumors, an overview of current GC methods, and collectively presents the scope and outlook of GC methods in oncology.

  15. [Identification of special quality eggs with NIR spectroscopy technology based on symbol entropy feature extraction method].

    Science.gov (United States)

    Zhao, Yong; Hong, Wen-Xue

    2011-11-01

    Fast, nondestructive and accurate identification of special quality eggs is an urgent problem. The present paper proposed a new feature extraction method based on symbol entropy to identify near infrared spectroscopy of special quality eggs. The authors selected normal eggs, free range eggs, selenium-enriched eggs and zinc-enriched eggs as research objects and measured the near-infrared diffuse reflectance spectra in the range of 12 000-4 000 cm(-1). Raw spectra were symbolically represented with aggregation approximation algorithm and symbolic entropy was extracted as feature vector. An error-correcting output codes multiclass support vector machine classifier was designed to identify the spectrum. Symbolic entropy feature is robust when parameter changed and the highest recognition rate reaches up to 100%. The results show that the identification method of special quality eggs using near-infrared is feasible and the symbol entropy can be used as a new feature extraction method of near-infrared spectra.

  16. New Method of Road Surface Identification in Anti-Lock Braking System

    Institute of Scientific and Technical Information of China (English)

    QI Zhi-quan; LIU Zhao-du; ZHANG Jing-bo; MA Yue-feng

    2005-01-01

    Based on the vehicle-road dynamic model, the road characteristic parameter, which depends on different types of road surfaces, is introduced and a new method of road surface identification for automotive anti-lock braking system (ABS) is proposed. According to the characteristics of vehicle-road dynamic model, a simple math resolution method of the model's factors is established. Only using the information of wheel speed, the vehicle reference velocity and the wheel slip ratio are estimated real-timely. And based on the wheel dynamic model, the road characteristic parameter is determined to identify the road surface for the determination of thresholds of ABS regulative parameters. With this new method, the road surface identification can be accurately obtained and calculation time is short that it can meet the ABS real time control need, and it also improves the performance of ABS.

  17. Extraction, enumeration and identification methods for monitoring microplastics in the environment

    Science.gov (United States)

    Qiu, Qiongxuan; Tan, Zhi; Wang, Jundong; Peng, Jinping; Li, Meimin; Zhan, Zhiwei

    2016-07-01

    There is much research on the occurrence, pollution characteristics and impacts of microplastics in the marine environment but this omits factors which play important roles in the analysis of microplastics. This review summarizes the methods and techniques in the extraction from sediment, seawater and organisms, and assesses their advantages and limitations according to different experimental conditions, such as salt solution and reagents added to remove organic matter. Similarly, this overview includes the enumeration methods of microplastics by many kinds of microscopes (e.g. stereomicroscope, fluorescent microscope, scanning electron microscope). Advantages and challenges of using micro-FTIR, ART-FTIR, FPA-FTIR, Pry-GC/MS, and Raman spectroscopy in the identification methods are also discussed. This review suggests that monitoring microplastics needs standardized protocols for extraction, identification and quantification and that further research on the effects of microplastics to human health is needed.

  18. [A method of hyperspectral quantificational identification of minerals based on infrared spectral artificial immune calculation].

    Science.gov (United States)

    Liu, Qing-Jie; Jing, Lin-Hai; Li, Xin-Wu; Bi, Jian-Tao; Wang, Meng-Fei; Lin, Qi-Zhong

    2013-04-01

    Rapid identification of minerals based on near infrared (NIR) and shortwave infrared (SWIR) hyperspectra is vital to remote sensing mine exploration, remote sensing minerals mapping and field geological documentation of drill core, and have leaded to many identification methods including spectral angle mapping (SAM), spectral distance mapping (SDM), spectral feature fitting(SFF), linear spectral mixture model (LSMM), mathematical combination feature spectral linear inversion model(CFSLIM) etc. However, limitations of these methods affect their actual applications. The present paper firstly gives a unified minerals components spectral inversion (MCSI) model based on target sample spectrum and standard endmember spectral library evaluated by spectral similarity indexes. Then taking LSMM and SAM evaluation index for example, a specific formulation of unified MCSI model is presented in the form of a kind of combinatorial optimization. And then, an artificial immune colonial selection algorithm is used for solving minerals feature spectral linear inversion model optimization problem, which is named ICSFSLIM. Finally, an experiment was performed to use ICSFSLIM and CFSLIM to identify the contained minerals of 22 rock samples selected in Baogutu in Xinjiang China. The mean value of correctness and validness identification of ICSFSLIM are 34.22% and 54.08% respectively, which is better than that of CFSLIM 31.97% and 37.38%; the correctness and validness variance of ICSFSLIM are 0.11 and 0.13 smaller than that of CFSLIM, 0.15 and 0.25, indicating better identification stability.

  19. IDENTIFICATION METHOD FOR PENDULUM SYSTEM MOMENT OF INERTIA WITH VISCOUS DAMPING

    Directory of Open Access Journals (Sweden)

    A. S. Alyshev

    2016-09-01

    Full Text Available The paper proposes a method for identification of axial moment of inertia of the mechanical system called reaction wheel pendulum with a viscous friction in the bearings of the suspension. The method is based on the reversible symmetric motions. Pendulum system motion includes a free measured motion and reverse symmetrical motion at the same angular interval. The pendulum includes a rod with a low-power DC motor with a flywheel attached to the end of the rod. The angle of rotation and velocity of the rod and the flywheel are measured by encoders. The paper introduces a new method,presents a design formula,a mathematical model of the pendulum system and a robust motor control law for it. The method is based on energy algorithm and control residing in electric motor operational changes by means of a flywheel. The mechanical system moves symmetrically that is provided by nonuniform controlled flywheel rotation. As a result, the influence of dissipative factors on identification results is eliminated. Dynamic modeling is carried out for the pendulum system and proves high accuracy of the method. The research results can be used for identification of complex mechanical systems under the action of resistance, dissipative and other forces.

  20. Alternative method for identification of the dynamic properties of bolted joints

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Tieneng [Beijing Univ. of Technology, Beijing (China); Li, Ling; Cai, Ligang; Zhao, Yong Sheng [Xi' an Univ. of Architecture Technology, Xi' an (China)

    2012-10-15

    Bolted joints often have a significant effect on the dynamical behavior of assembled mechanical structures. An accurate model of an assembled structure depends on correctly determining and identifying the dynamic parameters of bolted joints. This paper presents an alternative method for identifying these dynamic parameters using structure's natural frequency and damping. A novel experiment is designed with a test piece consisting of only bolted joints, with the governing equations of the test piece established using the analytical method. The relationships between the equivalent dynamic parameters of the bolted joints and the natural frequencies and damping ratios of the test piece are determined for both the normal and tangential directions. The parameter identification problem for bolted joints is thus transformed into a test of the natural frequency and the damping ratio of the test piece. In order to check the accuracy of the proposed identification method, the test piece and bolted joints are modeled using the finite element method (FEM) and the dynamic properties of the test piece are analyzed. The maximum error between the natural frequencies of the FEM result and the experimental values in the normal and tangential models are 4.73% and 0.34%, respectively. The result indicates that the proposed method is valid for the dynamic parameter identification of bolted joints.

  1. Development of Time-Reversal Method for Impact Source Identification on Plate Structures

    Directory of Open Access Journals (Sweden)

    Chunlin Chen

    2013-01-01

    Full Text Available This paper presents a detailed study on the impact source identification of a plate structure using time-reversal (T-R method. Prior to impact monitoring, the plate is calibrated (or characterized by transfer functions at discrete locations on the plate surface. Both impact location and impact loading time-history are identified using T-R technique and associated signal processing algorithms. Numerical verification for finite-size isotropic plates under low velocity impacts is performed to demonstrate the versatility of T-R method for impact source identification. The tradeoff between accuracy of the impact location detection and calibration spacing is studied in detail. In particular, the effect of plate thickness on calibration spacing has been examined. A number of parameters selected for determining the impact location, approximated transfer functions and steps taken for reconstructing the impact loading time-history are also examined. Two types of noise with various intensities contaminated in strain response and/or transfer functions are investigated for demonstrating the stability and reliability of the T-R method. The results show that T-R method is robust against noise in impact location detection and force reconstruction in circumventing the inherent ill-conditioned inverse problem. Only transfer functions are needed to be calibrated and four sensors are requested in T-R method for impact identification.

  2. Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis.

    Science.gov (United States)

    Koo, Byoung-Mo; Kritikos, George; Farelli, Jeremiah D; Todor, Horia; Tong, Kenneth; Kimsey, Harvey; Wapinski, Ilan; Galardini, Marco; Cabal, Angelo; Peters, Jason M; Hachmann, Anna-Barbara; Rudner, David Z; Allen, Karen N; Typas, Athanasios; Gross, Carol A

    2017-03-22

    A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria.

  3. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

    Directory of Open Access Journals (Sweden)

    Urchueguía Javier F

    2010-11-01

    Full Text Available Abstract Background Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. Results We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. Conclusions iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.

  4. Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes.

    Science.gov (United States)

    Baek, Songjoon; Sung, Myong-Hee

    2016-01-01

    High-throughput sequencing technologies have made it possible for biologists to generate genome-wide profiles of chromatin features at the nucleotide resolution. Enzymes such as nucleases or transposes have been instrumental as a chromatin-probing agent due to their ability to target accessible chromatin for cleavage or insertion. On the scale of a few hundred base pairs, preferential action of the nuclear enzymes on accessible chromatin allows mapping of cell state-specific accessibility in vivo. Such accessible regions contain functionally important regulatory sites, including promoters and enhancers, which undergo active remodeling for cells adapting in a dynamic environment. DNase-seq and the more recent ATAC-seq are two assays that are gaining popularity. Deep sequencing of DNA libraries from these assays, termed genomic footprinting, has been proposed to enable the comprehensive construction of protein occupancy profiles over the genome at the nucleotide level. Recent studies have discovered limitations of genomic footprinting which reduce the scope of detectable proteins. In addition, the identification of putative factors that bind to the observed footprints remains challenging. Despite these caveats, the methodology still presents significant advantages over alternative techniques such as ChIP-seq or FAIRE-seq. Here we describe computational approaches and tools for analysis of chromatin accessibility and genomic footprinting. Proper experimental design and assay-specific data analysis ensure the detection sensitivity and maximize retrievable information. The enzyme-based chromatin profiling approaches represent a powerful and evolving methodology which facilitates our understanding of how the genome is regulated.

  5. Genome-Scale Transcriptome Analysis of the Desert Shrub Artemisia sphaerocephala.

    Directory of Open Access Journals (Sweden)

    Lijing Zhang

    Full Text Available Artemisia sphaerocephala, a semi-shrub belonging to the Artemisia genus of the Compositae family, is an important pioneer plant that inhabits moving and semi-stable sand dunes in the deserts and steppes of northwest and north-central China. It is very resilient in extreme environments. Additionally, its seeds have excellent nutritional value, and the abundant lipids and polysaccharides in the seeds make this plant a potential valuable source of bio-energy. However, partly due to the scarcity of genetic information, the genetic mechanisms controlling the traits and environmental adaptation capacity of A. sphaerocephala are unknown.Here, we present the first in-depth transcriptomic analysis of A. sphaerocephala. To maximize the representation of conditional transcripts, mRNA was obtained from 17 samples, including living tissues of desert-growing A. sphaerocephala, seeds germinated in the laboratory, and calli subjected to no stress (control and high and low temperature, high and low osmotic, and salt stresses. De novo transcriptome assembly performed using an Illumina HiSeq 2500 platform resulted in the generation of 68,373 unigenes. We analyzed the key genes involved in the unsaturated fatty acid synthesis pathway and identified 26 A. sphaerocephala fad2 genes, which is the largest fad2 gene family reported to date. Furthermore, a set of genes responsible for resistance to extreme temperatures, salt, drought and a combination of stresses was identified.The present work provides abundant genomic information for functional dissection of the important traits of A. sphaerocephala and contributes to the current understanding of molecular adaptive mechanisms of A. sphaerocephala in the desert environment. Identification of the key genes in the unsaturated fatty acid synthesis pathway could increase understanding of the biological regulatory mechanisms of fatty acid composition traits in plants and facilitate genetic manipulation of the fatty acid

  6. Genome-Scale Transcriptome Analysis of the Desert Shrub Artemisia sphaerocephala.

    Science.gov (United States)

    Zhang, Lijing; Hu, Xiaowei; Miao, Xiumei; Chen, Xiaolong; Nan, Shuzhen; Fu, Hua

    2016-01-01

    Artemisia sphaerocephala, a semi-shrub belonging to the Artemisia genus of the Compositae family, is an important pioneer plant that inhabits moving and semi-stable sand dunes in the deserts and steppes of northwest and north-central China. It is very resilient in extreme environments. Additionally, its seeds have excellent nutritional value, and the abundant lipids and polysaccharides in the seeds make this plant a potential valuable source of bio-energy. However, partly due to the scarcity of genetic information, the genetic mechanisms controlling the traits and environmental adaptation capacity of A. sphaerocephala are unknown. Here, we present the first in-depth transcriptomic analysis of A. sphaerocephala. To maximize the representation of conditional transcripts, mRNA was obtained from 17 samples, including living tissues of desert-growing A. sphaerocephala, seeds germinated in the laboratory, and calli subjected to no stress (control) and high and low temperature, high and low osmotic, and salt stresses. De novo transcriptome assembly performed using an Illumina HiSeq 2500 platform resulted in the generation of 68,373 unigenes. We analyzed the key genes involved in the unsaturated fatty acid synthesis pathway and identified 26 A. sphaerocephala fad2 genes, which is the largest fad2 gene family reported to date. Furthermore, a set of genes responsible for resistance to extreme temperatures, salt, drought and a combination of stresses was identified. The present work provides abundant genomic information for functional dissection of the important traits of A. sphaerocephala and contributes to the current understanding of molecular adaptive mechanisms of A. sphaerocephala in the desert environment. Identification of the key genes in the unsaturated fatty acid synthesis pathway could increase understanding of the biological regulatory mechanisms of fatty acid composition traits in plants and facilitate genetic manipulation of the fatty acid composition of oil

  7. Investigation into on-road vehicle parameter identification based on subspace methods

    Science.gov (United States)

    Dong, Guangming; Chen, Jin; Zhang, Nong

    2014-12-01

    The randomness of road-tyre excitations can excite the low frequency ride vibrations of bounce, pitch and roll modes of an on-road vehicle. In this paper, modal parameters and mass moments of inertia of an on-road vehicle are estimated with an acceptable accuracy only by measuring accelerations of vehicle sprung mass and unsprung masses, which is based on subspace identification methods. The vehicle bounce, pitch and roll modes are characterized by their large damping (damping ratio 0.2-0.3). Two kinds of subspace identification methods, one that uses input/output data and the other that uses output data only, are compared for the highly damped modes. It is shown that, when the same data length is given, larger error of modal identification results can be clearly observed for the method using output data only; while additional use of input data will significantly reduce estimation variance. Instead of using tyre forces as inputs, which are difficult to be measured or estimated, vertical accelerations of unsprung masses are used as inputs. Theoretical analysis and Monte Carlo experiments show that, when the vehicle speed is not very high, subspace identification method using accelerations of unsprung masses as inputs can give more accurate results compared with the method using road-tyre forces as inputs. After the modal parameters are identified, and if vehicle mass and its center of gravity are pre-determined, roll and pitch moments of inertia of an on-road vehicle can be directly computed using the identified frequencies only, without requiring accurate estimation of mode shape vectors and multi-variable optimization algorithms.

  8. A Subspace Identification Method for Detecting Abnormal Behavior in HVAC Systems

    Directory of Open Access Journals (Sweden)

    Dimitris Sklavounos

    2015-01-01

    Full Text Available A method for the detection of abnormal behavior in HVAC systems is presented. The method combines deterministic subspace identification for each zone independently to create a system model that produces the anticipated zone’s temperature and the sequential test CUSUM algorithm to detect drifts of the rate of change of the difference between the real and the anticipated measurements. Simulation results regarding the detection of infiltration heat losses and the detection of exogenous heat gains such as fire demonstrate the effectiveness of the proposed method.

  9. Comparative studies in method for stratigraphical structure measurement of ice cores: Identification of cloudy bands

    Institute of Scientific and Technical Information of China (English)

    Morimasa Takata; Hitoshi Shoji; Atsushi Miyamoto; Kimiko Shimohara

    2003-01-01

    Cloudy bands are typical stratigraphic structure in deep ice core.Detailed recording of cloudy bands is important for dating of ice core since pair of series cloudy band and clear layer is corresponds to annual layer and it sometimes corresponds to volcanic ash layer.We developed two type scanners, transmitted light method and laser tomograph method for the stratigraphic study.Measurements were carried out for NGRIP deep ice core, which containing many cloudy bands, using the two type scanners and digital camera.We discussed about the possibility of identification of cloudy bands by each method and about advantage and disadvantage of measurements and their results.

  10. SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

    Institute of Scientific and Technical Information of China (English)

    Liu Yulin; Peng Qicong

    2002-01-01

    A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

  11. SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME—VARYING CHANNELS

    Institute of Scientific and Technical Information of China (English)

    LiuYulin; PengQicong

    2002-01-01

    A new blind method is proposed for identification of CDMA Time-Varyin(TV) channels in this paper.By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant(TI) coordingates is proposed.Unlike existing basis expansion methods, this new algorithm does not require estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols.The algorithm offers definite explanation of the expansion coordinates.Simulation demonstrates the effectiveness of the algorithm.

  12. The review of identification and assay methods of β-blockers

    Directory of Open Access Journals (Sweden)

    Ольга Олександрівна Віслоус

    2015-10-01

    Full Text Available Every year the number of β-blockers on the pharmaceutical market is increasing, requiring systematization of their standardization methods.Aim of research. The aim of our research is to study literature data about identification and assay methods of β-blockers with different direction of action – selective (praktolol, metoprolol, atenolol, acebutolol, betaxolol, bevantolol, bisoprolol, celiprolol, esmolol, epanolol, esatenolol, nebivolol, Talinolol, non-selective (alprenolol, Oxprenololum, pindolol, propranolol, timolol, sotalol, nadolol, mepindolol, karteol, tertatolol, bopindolol, bupranolol, penbutolol, kloranolol and combined (labetalol, carvedilol.Methods. The analytical review of literature sources about β-blockers analysis by physical, chemical, and physicochemical methods.Results. After literature sources’ analyzing it was found that physical and physicochemical constants are basically used for β-blockers pharmacopoeial analysis; both physicochemical values and chemical reactions are used in forensic analysis, resulting in the article.It was founded that titration methods, mostly acid-base titration method, are used for β-blockers assay in the analysis of substances. For β-blockers detection in biological fluids and dosage forms, active pharmaceutical ingredients and metabolites mixture separation one should prefer physicochemical methods, such as gas chromatography and liquid chromatography, absorption UV-Visible spectroscopy, fluorometry, etc.Conclusion. The results have shown can be used for the further search of the identification and assay optimal methods of β-blockers both pure and mixed with other active substances and excipients

  13. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data

    DEFF Research Database (Denmark)

    Clausen, Philip T. L. C.; Zankari, Ea; Aarestrup, Frank Møller;

    2016-01-01

    Next generation sequencing (NGS) may be an alternative to phenotypic susceptibility testing for surveillance and clinical diagnosis. However, current bioinformatics methods may be associated with false positives and negatives. In this study, a novel mapping method was developed and benchmarked...... to two different methods in current use for identification of antibiotic resistance genes in bacterial WGS data. A novel method, KmerResistance, which examines the co-occurrence of k-mers between the WGS data and a database of resistance genes, was developed. The performance of this method was compared...... with two previously described methods; ResFinder and SRST2, which use an assembly/BLAST method and BWA, respectively, using two datasets with a total of 339 isolates, covering five species, originating from the Oxford University Hospitals NHS Trust and Danish pig farms. The predicted resistance...

  14. Rapid microbiochemical method for presumptive identification of gastroenteritis-associated members of the family Enterobacteriaceae.

    Science.gov (United States)

    Yong, D C; Thompson, J S; Prytula, A

    1985-06-01

    A method for rapid screening of isolates of pathogenic members of the family Enterobacteriaceae is described. Flow charts are used in conjunction with triple sugar iron agar, o-nitrophenyl-beta-D-galactopyranoside-phenylalanine-motility sulfate screening media, oxidase test, and six rapid biochemical tests, namely, lysine decarboxylase, urease, indole, esculin hydrolysis, malonate, and xylose. This scheme is used to provide an inexpensive but rapid presumptive identification of Salmonella, Shigella, Edwardsiella, Aeromonas, Plesiomonas, Vibrio, and Yersinia isolates from stool cultures.

  15. Rapid microbiochemical method for presumptive identification of gastroenteritis-associated members of the family Enterobacteriaceae.

    OpenAIRE

    Yong, D C; Thompson, J. S.; Prytula, A

    1985-01-01

    A method for rapid screening of isolates of pathogenic members of the family Enterobacteriaceae is described. Flow charts are used in conjunction with triple sugar iron agar, o-nitrophenyl-beta-D-galactopyranoside-phenylalanine-motility sulfate screening media, oxidase test, and six rapid biochemical tests, namely, lysine decarboxylase, urease, indole, esculin hydrolysis, malonate, and xylose. This scheme is used to provide an inexpensive but rapid presumptive identification of Salmonella, Sh...

  16. An unusual method of forensic human identification: use of selfie photographs.

    Science.gov (United States)

    Miranda, Geraldo Elias; Freitas, Sílvia Guzella de; Maia, Luiza Valéria de Abreu; Melani, Rodolfo Francisco Haltenhoff

    2016-06-01

    As with other methods of identification, in forensic odontology, antemortem data are compared with postmortem findings. In the absence of dental documentation, photographs of the smile play an important role in this comparison. As yet, there are no reports of the use of the selfie photograph for identification purposes. Owing to advancements in technology, electronic devices, and social networks, this type of photograph has become increasingly common. This paper describes a case in which selfie photographs were used to identify a carbonized body, by using the smile line and image superimposition. This low-cost, rapid, and easy to analyze technique provides highly reliable results. Nevertheless, there are disadvantages, such as the limited number of teeth that are visible in a photograph, low image quality, possibility of morphological changes in the teeth after the antemortem image was taken, and difficulty of making comparisons depending on the orientation of the photo. In forensic odontology, new methods of identification must be sought to accompany technological evolution, particularly when no traditional methods of comparison, such as clinical record charts or radiographs, are available.

  17. Noncontact blood species identification method based on spatially resolved near-infrared transmission spectroscopy

    Science.gov (United States)

    Zhang, Linna; Sun, Meixiu; Wang, Zhennan; Li, Hongxiao; Li, Yingxin; Li, Gang; Lin, Ling

    2017-09-01

    The inspection and identification of whole blood are crucially significant for import-export ports and inspection and quarantine departments. In our previous research, we proved Near-Infrared diffuse transmitted spectroscopy method was potential for noninvasively identifying three blood species, including macaque, human and mouse, with samples measured in the cuvettes. However, in open sampling cases, inspectors may be endangered by virulence factors in blood samples. In this paper, we explored the noncontact measurement for classification, with blood samples measured in the vacuum blood vessels. Spatially resolved near-infrared spectroscopy was used to improve the prediction accuracy. Results showed that the prediction accuracy of the model built with nine detection points was more than 90% in identification between all five species, including chicken, goat, macaque, pig and rat, far better than the performance of the model built with single-point spectra. The results fully supported the idea that spatially resolved near-infrared spectroscopy method can improve the prediction ability, and demonstrated the feasibility of this method for noncontact blood species identification in practical applications.

  18. Research on identification method of heavy vehicle rollover based on hidden Markov model

    Science.gov (United States)

    Zhao, Zhiguo; Wang, Yeqin; Hu, Xiaoming; Tao, Yukai; Wang, Jinsheng

    2017-07-01

    Aiming at the problem of early warning credibility degradation as the heavy vehicle load and its center of gravity change greatly; the heavy vehicle rollover state identification method based on the Hidden Markov Model (HMM, is introduced to identify heavy vehicle lateral conditions dynamically in this paper. In this method, the lateral acceleration and roll angle are taken as the observation values of the model base. The Viterbi algorithm is used to predict the state sequence with the highest probability in the observed sequence, and the Markov prediction algorithm is adopted to calculate the state transition law and to predict the state of the vehicle in a certain period of time in the future. According to combination conditions of Double lane change and steering, applying Trucksim and Matlab trained hidden Markov model, the model is applied to the online identification of heavy vehicle rollover states. The identification results show that the model can accurately and efficiently identify the vehicle rollover state, and has good applicability. This study provides a novel method and a general strategy for active safety early warning and control of vehicles, which has reference significance for the application of the Hidden Markov theory in collision, rear-end and lane departure warning system.

  19. Revealing genome-scale transcriptional regulatory landscape of OmpR highlights its expanded regulatory roles under osmotic stress in Escherichia coli K-12 MG1655

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Gao, Ye; Kim, Donghyuk

    2017-01-01

    A transcription factor (TF), OmpR, plays a critical role in transcriptional regulation of the osmotic stress response in bacteria. Here, we reveal a genome-scale OmpR regulon in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs...

  20. High Resolution Ultrasonic Method for 3D Fingerprint Recognizable Characteristics in Biometrics Identification

    Science.gov (United States)

    Maev, R. Gr.; Bakulin, E. Yu.; Maeva, A.; Severin, F.

    Biometrics is a rapidly evolving scientific and applied discipline that studies possible ways of personal identification by means of unique biological characteristics. Such identification is important in various situations requiring restricted access to certain areas, information and personal data and for cases of medical emergencies. A number of automated biometric techniques have been developed, including fingerprint, hand shape, eye and facial recognition, thermographic imaging, etc. All these techniques differ in the recognizable parameters, usability, accuracy and cost. Among these, fingerprint recognition stands alone since a very large database of fingerprints has already been acquired. Also, fingerprints are key evidence left at a crime scene and can be used to indentify suspects. Therefore, of all automated biometric techniques, especially in the field of law enforcement, fingerprint identification seems to be the most promising. We introduce a newer development of the ultrasonic fingerprint imaging. The proposed method obtains a scan only once and then varies the C-scan gate position and width to visualize acoustic reflections from any appropriate depth inside the skin. Also, B-scans and A-scans can be recreated from any position using such data array, which gives the control over the visualization options. By setting the C-scan gate deeper inside the skin, distribution of the sweat pores (which are located along the ridges) can be easily visualized. This distribution should be unique for each individual so this provides a means of personal identification, which is not affected by any changes (accidental or intentional) of the fingers' surface conditions. This paper discusses different setups, acoustic parameters of the system, signal and image processing options and possible ways of 3-dimentional visualization that could be used as a recognizable characteristic in biometric identification.

  1. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

    OpenAIRE

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes...

  2. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

    Science.gov (United States)

    2016-03-15

    one antimicrobial or have human homologs (see Materials and Methods). This resulted in a set of 56 putative target reactions against P. aer- uginosa...diphosphate; amp, adenylate. See S1 Supporting Information for the definition of the remaining of the abbreviations. doi:10.1371/journal.pcbi.1004452.g001...reducing or biofilm-increasing reactions, respectively). Then, for the inhibition of each reaction not used in the definition of any one of the metabo

  3. Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Andrea De Martino

    2013-10-01

    Full Text Available Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail to eliminate them. As test cases, we tackle (a the problem of identifying infeasible cycles in the E. coli metabolic network and (b the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a are compared with previous analyses of the same issue, while results for (b are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.

  4. Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

    KAUST Repository

    Alzahrani, Majed A.

    2017-07-10

    Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such recently emerging high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. In this dissertation, we first demonstrate that detecting such “co-fit” gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype data sets for E. coli, proteobacteria, and yeast.

  5. System identification.Part H: Coupled identification concept and methods%系统辨识(8):耦合辨识概念与方法

    Institute of Scientific and Technical Information of China (English)

    丁锋

    2012-01-01

    耦合辨识是系统辨识的一个重要分支,是新近发展和提炼形成的一种辨识概念,主要用于研究结构复杂的参数耦合线性和非线性多变量系统的辨识问题辅助模型辨识思想、多新息辨识理论、递阶辨识原理、耦合辨识概念是本文作者提出的一些新的辨识研究思路、理念和方法,分别能够用于研究存在未知过程变量的不可测系统的辨识,能够提高辨识方法的收敛速度和参数估计精度,能够解决结构复杂、大规模多变量系统及参数耦合多变量系统的辨识问题、减小辨识算法的计算量.首先介绍多变量系统耦合辨识概念,在此基础上讨论多变量系统的几种(全)耦合最小二乘辨识方法、(全)耦合随机梯度辨识方法、部分耦合随机梯度辨识万法、部分耦合最小二乘辨识方法等,最后说明耦合辨识方法可推广用于有色噪声干扰多变量系统的辨识,并列出了一些多变量系统模型结构,阐述了耦合辨识概念可以结合辅助模型辨识思想、多新息辨识理论、递阶辨识原理、迭代搜索原理(梯度迭代、最小二乘迭代、牛顿迭代)等来研究线性或非线性多变量系统的辨识问题.%Coupled identification is an important branch of system identification and is a new identification concept which is used mainly to study identification problems of linear and nonlinear multivariable systems with complex structures and parameter coupling. The auxiliary model identification idea, the multi-innovation identification theory, the hierarchical identification principle,and the coupled identification conccpt are new identification research ideas, concepts and methods and can be used to study identification problems of systems with unknown process variables, to improve the convergence rates and accuracies of ident.ificat.ion methods,to solve identification problems of large-scale multivariable systems with complex structures and of

  6. Evaluation of the MIT RMID 1000 system for the identification of Listeria species:AOAC performance tested method 090325

    Science.gov (United States)

    The MIT 1000 RMID System is a rapid microbial identification device that uses the principles of light scattering coupled with proprietary algorithms to identify bacteria after being cultured and placed in a vial of filtered water. This specific method is for pure culture identification of Listeria ...

  7. A method for comparison of growth media in objective identification of Penicillium based on multi-spectral imaging

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder; Hansen, Michael Adsetts Edberg; Frisvad, Jens Christian

    2007-01-01

    We consider the problems of using excessive growth media for identification and performing objective identification of fungi at the species level. We propose a method for choosing the subset of growth media, which provides the best discrimination between several fungal species. Furthermore, we...

  8. Technical efficiency and economic viability of different cattle identification methods allowed by the Brazilian traceability system

    Directory of Open Access Journals (Sweden)

    Marcos Aurelio Lopes

    2017-03-01

    Full Text Available We aimed to evaluate the technical efficiency and economic viability of the implementation and use of four cattle identification methods allowed by the Brazilian traceability system. The study was conducted in a beef cattle production system located in the State of Mato Grosso, from January to June 2012. Four identification methods (treatments were compared: T1: ear tag in one ear and ear button in the other ear (eabu; T2: ear tag and iron brand on the right leg (eaib; T3: ear tag in one ear and tattoo on the other ear (eata; and T4: ear tag in one ear and electronic ear tag (eael on the other. Each treatment was applied to 60 Nelore animals, totaling 240 animals, divided equally into three life stages (calves, young cattle, adult cattle. The study had two phases: implementation (phase 1 and reading and transfer of identification numbers to an electronic database (phase 2. All operating expenses related to the two phases of the study were determined. The database was constructed, and the statistical analyses were performed using SPSS® 17.0 software. Regarding the time spent on implementation (phase 1, conventional ear tags and electronic ear tags produced similar results, which were lower than those of hot iron and tattoo methods, which differed from each other. Regarding the time required for reading the numbers on animals and their transcription into a database (phase 2, electronic ear-tagging was the fastest method, followed by conventional ear tag, hot iron and tattoo. Among the methods analyzed, the electronic ear tag had the highest technical efficiency because it required less time to implement identifiers and to complete the process of reading and transcription to an electronic database and because it did not exhibit any errors. However, the cost of using the electronic ear-tagging method was higher primarily due to the cost of the device.

  9. Identification of shear buildings using an instrumental variable method and linear integral filters

    Science.gov (United States)

    Concha, Antonio; Garrido, Rubén; Alvarez-Icaza, Luis

    2016-12-01

    This paper develops a method for estimating the parameters of a seismically excited building. Acceleration measurements of the ground and of the building floors, containing offsets and noise, are used for identification purposes. The proposed scheme estimates the complete model of the building if all the floors are equipped with accelerometers. Moreover, it also estimates a reduced model of the structure if only some floors are instrumented. The methodology is based on the combined use of the Instrumental Variable method and Linear Integral Filters. The Instrumental Variable method employs as instrument an auxiliary model of the structure, and it is able to directly identify the continuous-time structure model using discrete-time data without resorting on model transformations from continuous-time to discrete-time, and vice-versa. Using Linear Integral Filters allows obtaining a linear in the parameters expression that depends only on acceleration measurements suitable for parameter identification purposes. These filters eliminate measurement offsets and attenuate high-frequency measurement noise. The above features together with the use of the Instrumental variable method reduce the likelihood of biased parameter estimates. Experiments on a testbed employing a reduced-scale five-story structure allow comparing the results obtained using the Instrumental Variable method and those produced by the standard Least Squares method.

  10. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    Science.gov (United States)

    Tartakovsky, G. D.; Tartakovsky, A. M.; Scheibe, T. D.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

    2013-09-01

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model

  11. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    Energy Technology Data Exchange (ETDEWEB)

    Tartakovsky, Guzel D.; Tartakovsky, Alexandre M.; Scheibe, Timothy D.; Fang, Yilin; Mahadevan, Radhakrishnan; Lovley, Derek R.

    2013-09-07

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under

  12. A molecular method for S-allele identification in apple based on allele-specific PCR.

    Science.gov (United States)

    Janssens, G A; Goderis, I J; Broekaert, W F; Broothaerts, W

    1995-09-01

    cDNA sequences corresponding to two self-incompatibility alleles (S-alleles) of the apple cv 'Golden Delicious' have previously been described, and now we report the identification of three additional S-allele cDNAs of apple, one of which was isolated from a pistil cDNA library of cv 'Idared' and two of which were obtained by reverse transcription-PCR (RT-PCR) on pistil RNA of cv 'Queen's Cox'. A comparison of the deduced amino acid sequences of these five S-allele cDNAs revealed an average homology of 69%. Based on the nucleotide sequences of these S-allele cDNAs, we developed a molecular technique for the diagnostic identification of the five different S-alleles in apple cultivars. The method used consists of allele-specific PCR amplification of genomic DNA followed by digestion of the amplification product with an allele-specific restriction endonuclease. Analysis of a number of apple cultivars with known S-phenotype consistently showed coincidence of phenotypic and direct molecular data of the S-allele constitution of the cultivars. It is concluded that the S-allele identification approach reported here provides a rapid and useful method to determine the S-genotype of apple cultivars.

  13. Blood species identification using Near-Infrared diffuse transmitted spectra and PLS-DA method

    Science.gov (United States)

    Zhang, Linna; Zhang, Shengzhao; Sun, Meixiu; Wang, Zhennan; Li, Hongxiao; Li, Yingxin; Li, Gang; Lin, Ling

    2016-05-01

    Blood species identification is of great significance for blood supervision and wildlife investigations. The current methods used to identify the blood species are destructive. Near-Infrared spectroscopy method is known for its non-invasive properties. In this research, we combined Near-Infrared diffuse transmitted spectra and Partial Least Square Discrimination Analysis (PLS-DA) to identify three blood species, including macaque, human and mouse. Blind test and external test were used to assess the PLS-DA model. The model performed 100% accuracy in its identification between three blood species. This approach does not require a specific knowledge of biochemical features for each individual class but relies on a spectroscopic statistical differentiation of the whole components. This is the first time to demonstrate Near-Infrared diffuse transmitted spectra have the ability to identify the species of origin of blood samples. The results also support a good potential of absorption and scattering spectroscopy for species identification in practical applications for real-time detection.

  14. Robust polygon recognition method with similarity invariants applied to star identification

    Science.gov (United States)

    Hernández, E. Antonio; Alonso, Miguel A.; Chávez, Edgar; Covarrubias, David H.; Conte, Roberto

    2017-02-01

    In the star identification process the goal is to recognize a star by using the celestial bodies in its vicinity as context. An additional requirement is to avoid having to perform an exhaustive scan of the star database. In this paper we present a novel approach to star identification using similarity invariants. More specifically, the proposed algorithm defines a polygon for each star, using the neighboring celestial bodies in the field of view as vertices. The mapping is insensitive to similarity transformation; that is, the image of the polygon under the transformation is not affected by rotation, scaling or translations. Each polygon is associated with an essentially unique complex number. We perform an exhaustive experimental validation of the proposed algorithm using synthetic data generated from the star catalog with uniformly-distributed positional noise introduced to each star. The star identification method that we present is proven to be robust, achieving a recognition rate of 99.68% when noise levels of up to ± 424 μ radians are introduced to the location of the stars. In our tests the proposed algorithm proves that if a polygon match is found, it always corresponds to the star under analysis; no mismatches are found. In its present form our method cannot identify polygons in cases where there exist missing or false stars in the analyzed images, in those situations it only indicates that no match was found.

  15. A New Identification Method of Both Magnetization Characteristic and Parameters of an Unloaded Transformer

    Directory of Open Access Journals (Sweden)

    Petr Orsag

    2008-01-01

    Full Text Available In this paper a new method of identification of both the magnetization characteristic and the instantaneous parameters G(t and K(t of a single-phase transformer under a sinusoidal supply voltage is proposed. The instantaneous conductance G(t and inverse inductance K(t of the transformer cross section are determined by the scalar product of time functions. The magnetization characteristic is derived by means of the inverse inductance K(t. The method is practically applied to an isolating transformer.

  16. IDENTIFICATION METHOD FOR PENDULUM SYSTEM MOMENT OF INERTIA WITH VISCOUS DAMPING

    OpenAIRE

    A. S. Alyshev; Melnikov, V G; G.I. Melnikov

    2016-01-01

    The paper proposes a method for identification of axial moment of inertia of the mechanical system called reaction wheel pendulum with a viscous friction in the bearings of the suspension. The method is based on the reversible symmetric motions. Pendulum system motion includes a free measured motion and reverse symmetrical motion at the same angular interval. The pendulum includes a rod with a low-power DC motor with a flywheel attached to the end of the rod. The angle of rotation and velocit...

  17. Double-layered target and identification method of individual target correlated with evaporation residues

    Energy Technology Data Exchange (ETDEWEB)

    Kaji, D., E-mail: daiya@riken.jp; Morimoto, K.

    2015-08-21

    A double-layered target system and an identification method (target ID) for individual targets mounted on a rotating wheel using correlation with evaporation residues were newly developed for the study of superheavy elements (SHE). The target system can be used in three modes: conventional single-layered mode, double-layered mode, and energy-degrader mode. The target ID method can be utilized for masking a target, measuring an excitation function without changing the beam energy from the accelerator, and searching for SHE nuclides using multiple targets during a single irradiation.

  18. A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method

    Directory of Open Access Journals (Sweden)

    O. Tsakiridis

    2016-01-01

    Full Text Available A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique.

  19. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

    Directory of Open Access Journals (Sweden)

    Maike Kathrin Aurich

    2016-08-01

    Full Text Available Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools , we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

  20. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models.

    Science.gov (United States)

    Aurich, Maike K; Fleming, Ronan M T; Thiele, Ines

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

  1. [Optimization method of MOS sensor array for identification of traditional Chinese medicine based on electronic nose].

    Science.gov (United States)

    Zou, Hui-Qin; Liu, Yong; Tao, Ou; Lin, Hui; Su, Yu-Zhen; Lin, Xiang-Long; Yan, Yong-Hong

    2013-01-01

    Optimization of sensor array is a significant topic in the application of electronic nose (EN). Stepwise discriminant analysis and cluster analysis combining with screening of typical index were employed to optimize the original array in the classification of 100 samples from 10 kinds of traditional Chinese medicine based on alpha-FOX3000 EN. And the identification ability was evaluated by three algorithm including principle component analysis, Fisher discriminant analysis and random forest. The results showed that the identification ability of EN was improved since not only the effective information was maintained but also the redundant one was eliminated by the optimized array. The optimized method was eventually established, it was accurate and efficient. And the optimized array was built up, that is, S1, S2, S5, S6, S8, S12.

  2. Using constitutive equation gap method for identification of elastic material parameters: Technical insights and illustrations

    KAUST Repository

    Florentin, Éric

    2011-08-09

    The constitutive equation gap method (CEGM) is a well-known concept which, until now, has been used mainly for the verification of finite element simulations. Recently, CEGM-based functional has been proposed to identify local elastic parameters based on experimental full-field measurement. From a technical point of view, this approach requires to quickly describe a space of statically admissible stress fields. We present here the technical insights, inspired from previous works in verification, that leads to the construction of such a space. Then, the identification strategy is implemented and the obtained results are compared with the actual material parameters for numerically generated benchmarks. The quality of the identification technique is demonstrated that makes it a valuable tool for interactive design as a way to validate local material properties. © 2011 Springer-Verlag.

  3. IDENTIFICATION AND MONITORING OF NOISE SOURCES OF CNC MACHINE TOOLS BY ACOUSTIC HOLOGRAPHY METHODS

    Directory of Open Access Journals (Sweden)

    Jerzy Józwik

    2016-06-01

    Full Text Available The paper presents the analysis of sound field emitted by selected CNC machine tools. The identification of noise sources and level was measured by acoustic holography for the 3-axis DMC 635eco machine tool and the 5-axis vertical machining centre DMU 65 monoBlock. The acoustic holography method allows precise identification and measurement of noise sources at different bandwidths of frequency. Detection of noise sources in tested objects allows diagnosis of their technical condition, as well as choice of effective means of noise reduction, which is highly significant from the perspective of minimising noise at the CNC machine operator workstation. Test results were presented as acoustic maps in various frequency ranges. Noise sources of the machine tool itself were identified, as well as the range of noise influence and the most frequent places of reflections and their span. The results of measurements were presented in figures and diagrams.

  4. Designing intracellular metabolism for production of target compounds by introducing a heterologous metabolic reaction based on a Synechosystis sp. 6803 genome-scale model.

    Science.gov (United States)

    Shirai, Tomokazu; Osanai, Takashi; Kondo, Akihiko

    2016-01-18

    Designing optimal intracellular metabolism is essential for using microorganisms to produce useful compounds. Computerized calculations for flux balance analysis utilizing a genome-scale model have been performed for such designs. Many genome-scale models have been developed for different microorganisms. However, optimal designs of intracellular metabolism aimed at producing a useful compound often utilize metabolic reactions of only the host microbial cells. In the present study, we added reactions other than the metabolic reactions with Synechosystis sp. 6803 as a host to its genome-scale model, and constructed a metabolic model of hybrid cells (SyHyMeP) using computerized analysis. Using this model provided a metabolic design that improves the theoretical yield of succinic acid, which is a useful compound. Constructing the SyHyMeP model enabled new metabolic designs for producing useful compounds. In the present study, we developed a metabolic design that allowed for improved theoretical yield in the production of succinic acid during glycogen metabolism by Synechosystis sp. 6803. The theoretical yield of succinic acid production using a genome-scale model of these cells was 1.00 mol/mol-glucose, but use of the SyHyMeP model enabled a metabolic design with which a 33 % increase in theoretical yield is expected due to the introduction of isocitrate lyase, adding activations of endogenous tree reactions via D-glycerate in Synechosystis sp. 6803. The SyHyMeP model developed in this study has provided a new metabolic design that is not restricted only to the metabolic reactions of individual microbial cells. The concept of construction of this model requires only replacement of the genome-scale model of the host microbial cells and can thus be applied to various useful microorganisms for metabolic design to produce compounds.

  5. A Parameter Identification Method to Determine Salinity of Sea Ice Using Temperature and Thickness Observations

    Science.gov (United States)

    Lv, Wei; Li, Xiaojiao; Feng, Enmin

    2014-02-01

    This study is intended to provide a parameter identification method to determine the salinity of sea ice using temperature and thickness measurements. This method is particularly effective when field data are sparse and unsatisfactory due to the difficulties associated with fieldwork, especially during the polar winter. The main idea of the method is described. The salinity profile is calculated by the temperature and thickness observations, which were measured at Nella Fjord around Zhongshan Station, Antarctica during the polar night time by the 22nd Chinese Antarctic Research Expedition. Another simulation for temperature profiles during a different measurement period is performed. Results show that better simulations of the salinity and temperature distributions are possible with the estimated parameters than with Eicken's and THESCI's methods. This method will help people to understand the salinity evolution of sea ice more thoroughly.

  6. Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods

    Directory of Open Access Journals (Sweden)

    Chu Zhang

    2016-01-01

    Full Text Available The potential of using mid-infrared transmittance spectroscopy combined with pattern recognition algorithm to identify coffee variety was investigated. Four coffee varieties in China were studied, including Typica Arabica coffee from Yunnan Province, Catimor Arabica coffee from Yunnan Province, Fushan Robusta coffee from Hainan Province, and Xinglong Robusta coffee from Hainan Province. Ten different pattern recognition methods were applied on the optimal wavenumbers selected by principal component analysis loadings. These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine, methods of medium effectiveness (partial least squares-discrimination analysis, K nearest neighbors, and random forest, and methods of low effectiveness (Naive Bayes classifier according to the classification accuracy for coffee variety identification.

  7. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

    Science.gov (United States)

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-06-18

    Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

  8. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient

    Directory of Open Access Journals (Sweden)

    Loraine Ann

    2008-06-01

    Full Text Available Abstract Background Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. Results In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC, that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. Conclusion

  9. Reconstructing the Backbone of the Saccharomycotina Yeast Phylogeny Using Genome-Scale Data

    Directory of Open Access Journals (Sweden)

    Xing-Xing Shen

    2016-12-01

    Full Text Available Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multilocus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing nine of the 11 known major lineages and 10 nonyeast fungal outgroups to generate a 1233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence and two data matrices (amino acids or the first two codon positions yielded identical and highly supported relationships between the nine major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, eight of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and the species Ascoidea rubescens. These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at http://y1000plus.org/blast.

  10. Radioisotope identification method for poorly resolved gamma-ray spectrum of nuclear security concern

    Energy Technology Data Exchange (ETDEWEB)

    Ninh, Giang Nguyen; Phongphaeth, Pengvanich, E-mail: phongphaeth.p@chula.ac.th; Nares, Chankow [Nuclear Engineering Department, Faculty of Engineering, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330 (Thailand); Hao, Quang Nguyen [Vietnam Atomic Energy Institute, Ministry of Science and Technology, Hanoi (Viet Nam)

    2016-01-22

    Gamma-ray signal can be used as a fingerprint for radioisotope identification. In the context of radioactive and nuclear materials security at the border control point, the detection task can present a significant challenge due to various constraints such as the limited measurement time, the shielding conditions, and the noise interference. This study proposes a novel method to identify the signal of one or several radioisotopes from a poorly resolved gamma-ray spectrum. In this method, the noise component in the raw spectrum is reduced by the wavelet decomposition approach, and the removal of the continuum background is performed using the baseline determination algorithm. Finally, the identification of radioisotope is completed using the matrix linear regression method. The proposed method has been verified by experiments using the poorly resolved gamma-ray signals from various scenarios including single source, mixing of natural uranium with five of the most common industrial radioactive sources (57Co, 60Co, 133Ba, 137Cs, and 241Am). The preliminary results show that the proposed algorithm is comparable with the commercial method.

  11. Radioisotope identification method for poorly resolved gamma-ray spectrum of nuclear security concern

    Science.gov (United States)

    Ninh, Giang Nguyen; Phongphaeth, Pengvanich; Nares, Chankow; Hao, Quang Nguyen

    2016-01-01

    Gamma-ray signal can be used as a fingerprint for radioisotope identification. In the context of radioactive and nuclear materials security at the border control point, the detection task can present a significant challenge due to various constraints such as the limited measurement time, the shielding conditions, and the noise interference. This study proposes a novel method to identify the signal of one or several radioisotopes from a poorly resolved gamma-ray spectrum. In this method, the noise component in the raw spectrum is reduced by the wavelet decomposition approach, and the removal of the continuum background is performed using the baseline determination algorithm. Finally, the identification of radioisotope is completed using the matrix linear regression method. The proposed method has been verified by experiments using the poorly resolved gamma-ray signals from various scenarios including single source, mixing of natural uranium with five of the most common industrial radioactive sources (57Co, 60Co, 133Ba, 137Cs, and 241Am). The preliminary results show that the proposed algorithm is comparable with the commercial method.

  12. New identification method for Hammerstein models based on approximate least absolute deviation

    Science.gov (United States)

    Xu, Bao-Chang; Zhang, Ying-Dan

    2016-07-01

    Disorder and peak noises or large disturbances can deteriorate the identification effects of Hammerstein non-linear models when using the least-square (LS) method. The least absolute deviation technique can be used to resolve this problem; however, its absolute value cannot meet the need of differentiability required by most algorithms. To improve robustness and resolve the non-differentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function that exhibits the characteristics of absolute value under certain situations. A new identification method for Hammerstein models based on ALAD is thus developed in this paper. The basic idea of this method is to apply the stochastic approximation theory in the process of deriving the recursive equations. After identifying the parameter matrix of the Hammerstein model via the new algorithm, the product terms in the matrix are separated by calculating the average values. Finally, algorithm convergence is proven by applying the ordinary differential equation method. The proposed algorithm has a better robustness as compared to other LS methods, particularly when abnormal points exist in the measured data. Furthermore, the proposed algorithm is easier to apply and converges faster. The simulation results demonstrate the efficacy of the proposed algorithm.

  13. A new closed-loop output error method for parameter identification of robot dynamics

    CERN Document Server

    Gautier, Maxime; Vandanjon, Pierre-Olivier

    2010-01-01

    Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method requires only the joint force/torque measurement. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference t...

  14. Mimo Lms-Armax Identification of Vibrating STRUCTURES—PART i: the Method

    Science.gov (United States)

    Fassois, S. D.

    2001-07-01

    A comprehensive linear multi stage autoregressive moving average with exogenous excitation (LMS-ARMAX) method for effective multiple-input multiple-output (MIMO) structural dynamics identification in the presence of noise is introduced. The method consists of (a) a vector ARMAX representation of an appropriate form, (b) effective LMS parameter estimation, (c) statistical order selection/validation, and (d) a digital dispersion analysis (DA) methodology for effective modal characterization. The LMS-ARMAX method overcomes many of the difficulties that had rendered MIMO ARMAX identification difficult in the past, featuring modest computational complexity, high accuracy, guaranteed algorithmic and model stability, and thus applicability to higher-dimensional problems and lightly damped structures, accurate modal parameter extraction, and effective distinction of structural from 'extraneous' modes. A critical assessment of the LMS-ARMAX method under various noise conditions, as well as comparisons with a simpler ARX version and the ERA (Eigensystem Realization Algorithm), are undertaken based upon experimental vibration data obtained from a scale aircraft skeleton structure. The paper is divided into two parts: The LMS-ARMAX method is presented in the first, and its critical assessment and comparisons in the second.

  15. A model selection method for nonlinear system identification based FMRI effective connectivity analysis.

    Science.gov (United States)

    Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Benali, Habib

    2011-07-01

    In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

  16. Review: High-performance computing to detect epistasis in genome scale data sets.

    Science.gov (United States)

    Upton, Alex; Trelles, Oswaldo; Cornejo-García, José Antonio; Perkins, James Richard

    2016-05-01

    It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

  17. Singular value decomposition of genome-scale mRNA lengths distribution reveals asymmetry in RNA gel electrophoresis band broadening.

    Science.gov (United States)

    Alter, Orly; Golub, Gene H

    2006-08-01

    We describe the singular value decomposition (SVD) of yeast genome-scale mRNA lengths distribution data measured by DNA microarrays. SVD uncovers in the mRNA abundance levels data matrix of genes x arrays, i.e., electrophoretic gel migration lengths or mRNA lengths, mathematically unique decorrelated and decoupled "eigengenes." The eigengenes are the eigenvectors of the arrays x arrays correlation matrix, with the corresponding series of eigenvalues proportional to the series of the "fractions of eigen abundance." Each fraction of eigen abundance indicates the significance of the corresponding eigengene relative to all others. We show that the eigengenes fit "asymmetric Hermite functions," a generalization of the eigenfunctions of the quantum harmonic oscillator and the integral transform which kernel is a generalized coherent state. The fractions of eigen abundance fit a geometric series as do the eigenvalues of the integral transform which kernel is a generalized coherent state. The "asymmetric generalized coherent state" models the measured data, where the profiles of mRNA abundance levels of most genes as well as the distribution of the peaks of these profiles fit asymmetric Gaussians. We hypothesize that the asymmetry in the distribution of the peaks of the profiles is due to two competing evolutionary forces. We show that the asymmetry in the profiles of the genes might be due to a previously unknown asymmetry in the gel electrophoresis thermal broadening of a moving, rather than a stationary, band of RNA molecules.

  18. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

    Directory of Open Access Journals (Sweden)

    Feist Adam M

    2011-10-01

    Full Text Available Abstract Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies.

  19. Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in Arabidopsis.

    Science.gov (United States)

    Töpfer, Nadine; Caldana, Camila; Grimbs, Sergio; Willmitzer, Lothar; Fernie, Alisdair R; Nikoloski, Zoran

    2013-04-01

    Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.

  20. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

    Science.gov (United States)

    Price, Nathan D; Thiele, Ines; Palsson, Bernhard Ø

    2006-06-01

    Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the "loop law" for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori.

  1. Flux balance analysis of genome-scale metabolic model of rice (Oryza sativa): Aiming to increase biomass

    Indian Academy of Sciences (India)

    Rahul Shaw; Sudip Kundu

    2015-10-01

    Due to socio-economic reasons, it is essential to design efficient stress-tolerant, more nutritious, high yielding rice varieties. A systematic understanding of the rice cellular metabolism is essential for this purpose. Here, we analyse a genome-scale metabolic model of rice leaf using Flux Balance Analysis to investigate whether it has potential metabolic flexibility to increase the biosynthesis of any of the biomass components. We initially simulate the metabolic responses under an objective to maximize the biomass components. Using the estimated maximum value of biomass synthesis as a constraint, we further simulate the metabolic responses optimizing the cellular economy. Depending on the physiological conditions of a cell, the transport capacities of intracellular transporters (ICTs) can vary. To mimic this physiological state, we randomly vary the ICTs’ transport capacities and investigate their effects. The results show that the rice leaf has the potential to increase glycine and starch in a wide range depending on the ICTs’ transport capacities. The predicted biosynthesis pathways vary slightly at the two different optimization conditions. With the constraint of biomass composition, the cell also has the metabolic plasticity to fix a wide range of carbon-nitrogen ratio.

  2. Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

    Science.gov (United States)

    Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

    2015-01-01

    Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

  3. Genome-scale transcriptome analysis in response to nitric oxide in birch cells: implications of the triterpene biosynthetic pathway.

    Directory of Open Access Journals (Sweden)

    Fansuo Zeng

    Full Text Available Evidence supporting nitric oxide (NO as a mediator of plant biochemistry continues to grow, but its functions at the molecular level remains poorly understood and, in some cases, controversial. To study the role of NO at the transcriptional level in Betula platyphylla cells, we conducted a genome-scale transcriptome analysis of these cells. The transcriptome of untreated birch cells and those treated by sodium nitroprusside (SNP were analyzed using the Solexa sequencing. Data were collected by sequencing cDNA libraries of birch cells, which had a long period to adapt to the suspension culture conditions before SNP-treated cells and untreated cells were sampled. Among the 34,100 UniGenes detected, BLASTX search revealed that 20,631 genes showed significant (E-values≤10-5 sequence similarity with proteins from the NR-database. Numerous expressed sequence tags (i.e., 1374 were identified as differentially expressed between the 12 h SNP-treated cells and control cells samples: 403 up-regulated and 971 down-regulated. From this, we specifically examined a core set of NO-related transcripts. The altered expression levels of several transcripts, as determined by transcriptome analysis, was confirmed by qRT-PCR. The results of transcriptome analysis, gene expression quantification, the content of triterpenoid and activities of defensive enzymes elucidated NO has a significant effect on many processes including triterpenoid production, carbohydrate metabolism and cell wall biosynthesis.

  4. Genome-scale prediction of proteins with long intrinsically disordered regions.

    Science.gov (United States)

    Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

    2014-01-01

    Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

  5. High effective THz-TDS method for the detection and identification of substances in real conditions

    Science.gov (United States)

    Trofimov, Vyacheslav A.; Varentsova, Svetlana A.; Tikhomirov, Vasiliy V.; Trofimov, Vladislav V.

    2016-05-01

    Low efficiency of the standard THz-TDS method for the detection and identification of substances is demonstrated. For this purpose, we use a few examples. In the first example, we model the noisy THz signals transmitted through the amphetamine-type stimulant in real conditions. Namely, with a temperature 18° C, the relative humidity of about 50 % and the distance between the parabolic mirror and the object about of 3.5 meters. We show that the standard THz-TDS method reveals the spectral features of many neutral substances and explosives in the noisy THz signals from the illicit stimulant MA, at the same time this method is not able to detect the presence of this stimulant in the noisy signals. The second example is the detection and identification of plastids with inhomogeneous surface in reflection mode. We show that inhomogeneous surface distorts spectral characteristics of the reflected THz signal main pulse, which cannot be used for the detection and identification of the plastids by means of the THz TDS method. In the last example we show that even under laboratory conditions (at short distance from the receiver), THz TDS detects in the semiconductors the absorption frequencies, which belong to both hazardous and neutral substances. To overcome this disadvantage, we propose to use the time-dependent spectrum of the THz pulse, transmitted through or reflected from a substance. For quality assessment of the presence of the standard substance absorption frequency in the signal under analysis, we use time-dependent integral correlation criteria. The influence of aperture placed in front of the sample on spectral properties of silicon wafers with different resistivity is demonstrated as well.

  6. Strategy for identification & characterization of Bartonella henselae with conventional & molecular methods

    Directory of Open Access Journals (Sweden)

    Kavita Diddi

    2013-01-01

    Full Text Available Background & objectives: Bartonella henselae is a fastidious gram-negative bacterium usually causing self limiting infections in immunocompetent individuals but often causes potentially life threatening infection, such as bacillary angiomatosis in immunocompromised patients. Both diagnosis of infections and research into molecular mechanisms of pathogenesis have been hindered by lack of appropriate and reliable diagnostic techniques. We undertook this study to standardize methods to characterize B. henselae in clinical samples to diagnose Bartonella infection correctly. Methods: B. henselae ATCC 49882 strain was procured from American type culture collection, USA. This strain was revived and maintained in the laboratory, and identification and characterization of this strain was done by conventional and molecular techniques, which included culture on various media, staining by different methods including electron microscopy, biochemical analysis by conventional methods and API, polymerase chain reaction (PCR for amplification of citrate synthase gene followed by restriction fragment length polymorphism (RFLP. Results: This organism was biochemically inert due to slow growth and generated unique identification code with API. The amplification of the citrate-synthase gene with primers yielded a 381 bp product followed by specific RFLP profile for B. henselae. Interpretation & conclusions: Bartonella is fastidious and fragile organism and should be handled carefully. Extra effort and careful observation are required to isolate and characterize this organism.

  7. An efficient method to reduce ill-posedness for structural dynamic load identification

    Science.gov (United States)

    Liu, Jie; Meng, Xianghua; Zhang, Dequan; Jiang, Chao; Han, Xu

    2017-10-01

    For the inverse problem of structural dynamic load identification, high system ill-posedness is a main cause leading to instability and low accuracy. In this study, an efficient interpolation-based method is proposed to reduce ill-posedness availably and identify dynamic load stably. The load history is discretized into a series of time elements, and the load profile in each time element is approximated through interpolation functions. Then, in the whole time domain, the dynamic responses under interpolation function loads are calculated through a few finite element analysis and then assembled together to form a global kernel function matrix for load identification. Using singular value decomposition (SVD), the ill-posed degree of the global kernel function matrix can be analyzed. Compared with the conventional Green kernel function method (GKFM), the ill-posedness of global kernel function matrix in the proposed method is significantly reduced. Especially, when the length of time element is selected appropriately, the global kernel function matrix is entirely well-posed and the corresponding dynamic load can be stably identified without any regularization operation. Numerical examples demonstrate the effectiveness of the proposed method and the correctness of identified load.

  8. Fast identification of microplastics in complex environmental samples by a thermal degradation method.

    Science.gov (United States)

    Dümichen, Erik; Eisentraut, Paul; Bannick, Claus Gerhard; Barthel, Anne-Kathrin; Senz, Rainer; Braun, Ulrike

    2017-05-01

    In order to determine the relevance of microplastic particles in various environmental media, comprehensive investigations are needed. However, no analytical method exists for fast identification and quantification. At present, optical spectroscopy methods like IR and RAMAN imaging are used. Due to their time consuming procedures and uncertain extrapolation, reliable monitoring is difficult. For analyzing polymers Py-GC-MS is a standard method. However, due to a limited sample amount of about 0.5 mg it is not suited for analysis of complex sample mixtures like environmental samples. Therefore, we developed a new thermoanalytical method as a first step for identifying microplastics in environmental samples. A sample amount of about 20 mg, which assures the homogeneity of the sample, is subjected to complete thermal decomposition. The specific degradation products of the respective polymer are adsorbed on a solid-phase adsorber and subsequently analyzed by thermal desorption gas chromatography mass spectrometry. For certain identification, the specific degradation products for the respective polymer were selected first. Afterwards real environmental samples from the aquatic (three different rivers) and the terrestrial (bio gas plant) systems were screened for microplastics. Mainly polypropylene (PP), polyethylene (PE) and polystyrene (PS) were identified for the samples from the bio gas plant and PE and PS from the rivers. However, this was only the first step and quantification measurements will follow.

  9. Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method

    Science.gov (United States)

    Chen, Can; Li, Tienan; Sun, Jian; Chen, Feng

    2016-01-01

    Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the crash occurrence probability for all exposure vehicles is estimated based on the empirical Bayesian method. As for the consequences of crashes, crashes may not only cause direct losses (e.g., occupant injuries and property damages) but also result in indirect losses. The indirect losses are expressed by the extra delays calculated using the deterministic queuing diagram method. The direct losses and indirect losses are uniformly monetized to be considered as the consequences of this risk. The potential costs of crashes, as a criterion to rank high-risk sites, can be explicitly expressed as the sum of the crash probability for all passing vehicles and the corresponding consequences of crashes. A case study on the urban expressways of Shanghai is presented. The results show that the new QRA method for HSID enables the identification of a set of high-risk sites that truly reveal the potential crash costs to society.

  10. Robust identification method for nonlinear model structures and its application to high-performance aircraft

    Science.gov (United States)

    Shi, Zhong-Ke; Wu, Fang-Xiang

    2013-06-01

    A common assumption is that the model structure is known for modelling high performance aircraft. In practice, this is not the case. Actually, structure identification plays the most important role in the processing of nonlinear system modelling. The integration of mode structure identification and parameter estimation is an efficient method to construct the model for high performance aircraft, which is nonlinear and also contains uncertainties. This article presents an efficient method for identifying nonlinear model structure and estimating parameters for high-performance aircraft model, which contains uncertainties. The parameters associated with nonlinear terms are considered one after the other if they should be included in the nonlinear model until a stopping criterion is met, which is based on Akaike's information criterion. A numerically efficient U-D factorisation is presented to avoid complex computation of high-order matrices. The proposed method is applied to flight test data of a high-performance aircraft. The results demonstrate that the proposed method could obtain the good aircraft model with a reasonably good fidelity based on the comparison with flight test data.

  11. RecRWR: a recursive random walk method for improved identification of diseases.

    Science.gov (United States)

    Arrais, Joel Perdiz; Oliveira, José Luís

    2015-01-01

    High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile. Several computational methods applied to physical interaction of protein networks have been successfully used in identification of the best disease candidates for each expression profile. An open problem for these methods is the ability to combine and take advantage of the wealth of biomedical data publicly available. We propose an enhanced method to improve selection of the best disease targets for a multilayer biomedical network that integrates PPI data annotated with stable knowledge from OMIM diseases and GO biological processes. We present a comprehensive validation that demonstrates the advantage of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach, RecRWR, in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available.

  12. Identification of Hidden Failures in Process Control Systems Based on the HMG Method

    DEFF Research Database (Denmark)

    Jalashgar, Atoosa

    1998-01-01

    This paper presents a function-oriented system analysis method that gains knowledge about properties of technical systems, including system capabilities that can appear as sources of incipient failures. Such failures have a significant role in connection with process control systems, since they can...... cause the systems to become overloaded and even unstable, if they remain hidden. The method uses a particular terminology to contribute to the identification of system properties, including goals, functions, and the capabilities. All identified knowledge about the system is then represented by using...... a tailored combination of two function-oriented methods, Multilevel Flow Modelling (MFM) and Goal Tree-Success Tree (GTST). The features of the method, called Hybrid MFM-GTST, are described and demonstrated by using an example of a process control system. (C) 1998 John Wiley & Sons, Inc....

  13. Dynamic method of stiffness identification in impacting systems for percussive drilling applications

    Science.gov (United States)

    Liao, Maolin; Ing, James; Sayah, Mukthar; Wiercigroch, Marian

    2016-12-01

    This paper introduces a dynamic method for the stiffness identification of an impacted object via analysis of its corresponding impact duration. To accurately detect the impact durations from experimental signals, nonlinear time series methods are applied. Two low-dimensional dynamical systems, including a piecewise-linear impact oscillator and a rock impacting system, are studied experimentally and numerically to demonstrate the proposed method. Meanwhile, the analytical prediction of the impact duration for the period-one one-impact motion is developed. The results of both systems indicate that, for a certain stiffness, the impact duration of the period-one one-impact motion is nearly constant. The higher the stiffness, the lower the impact duration. This monotone correlation provides a mechanism to estimate the stiffness of the impacted object once the impact duration has been accurately detected. The developed method can be used to optimise percussive drilling parameters.

  14. Identification of fungal pathogens in Formalin-fixed, Paraffin-embedded tissue samples by molecular methods.

    Science.gov (United States)

    Rickerts, Volker

    2016-02-01

    The etiology of invasive fungal infections (IFI) is incompletely understood due to diagnostic limitations including insensitivity of cultures and failure of histopathology to discriminate between different species. This diagnostic gap precludes the optimal use of antifungals, leading to adverse patient outcomes. The identification of fungal pathogens from Formalin-fixed, Paraffin-embedded tissue (FFPE) blocks by molecular methods is emerging as an alternative approach to study the etiology of IFI. PCR assays, including species specific- and broadrange fungal tests are used with FFPE samples from patients with proven IFI. Fungal species identification is achieved in 15-90% of the samples. This heterogeneity may be explained by the samples studied. However, comparison of different studies is impaired, as controls ruling out false positive-, false negative test results or PCR inhibition are frequently not reported. Studies using in situ hybridization also vary in the clinical samples included and the targeted fungi. In addition, target sequences, the probe chemistry and the detection of hybridization signals also account for the differences in diagnostic sensitivity. Using both approaches in parallel yields additive insights, potentially leading to a superior identification of fungal etiology and awareness of the limitations of both molecular diagnostic approaches.

  15. [Reproductibility of the morphological identification of schisocytes and evaluation of non observer-dependent methods].

    Science.gov (United States)

    Lesesve, J-F; Lecompte, T; Alla, F; Fenneteau, O; Cynober, T; Siest, J-P; Troussard, X; Flandrin, G

    2005-01-01

    Schistocytes are red blood cell fragments observed on a blood smear. They are a mark of mechanical haemolytic anaemias whose group of the thrombotic micro-angiopathies requires an urgent treatment. The detection of the schistocytes and sometimes their quantification are thus of primary importance. To evaluate this search for schistocytes, several surveys of practice were carried out (1999-2003) including pictures of blood fields (identification of schistocytes among abnormal red blood cells) near biologists of variable level of specialization. The aim was to try to lead to a consensus, in particular for the morphological criterias of identification. Our results indicated that: 1) the biologists are badly sensitized with the importance of this research and the consequences of their response for the diagnosis; 2) the morphological identification of the schistocytes is difficult with an important variability of the criteria according to the observers. An investigation overviewed by the French Group of Cellular Hematology (Delphi method) allowed the development of a morphological consensus (fragments of triangular/crescent/helmet forms with rectilinear zone testifying to the zone of break). In order to cancel the observer-dependent identification of the schistocytes, a software of morphometric analysis (Q-WIN, Leica) was developed for sorting, starting from digitalized microscopic fields, the fragmented - among the normal - red blood cells. The results appeared encouraging, but not yet optimized. An automated analyzer (Bayer ADVIA 120) was also evaluated for the measurement of the schizocytes ("fragmented red blood cells" parameter). The moderate over-estimation of the real schizocytes (+ 0,4%) encouraged to observe the clinical value of the fragmented red blood cells detection in a group of patients that undergone a bone-marrow transplantation. The predictive value of the test (98%) was satisfactory.

  16. Chaotic Time Series Analysis Method Developed for Stall Precursor Identification in High-Speed Compressors

    Science.gov (United States)

    1997-01-01

    A new technique for rotating stall precursor identification in high-speed compressors has been developed at the NASA Lewis Research Center. This pseudo correlation integral method uses a mathematical algorithm based on chaos theory to identify nonlinear dynamic changes in the compressor. Through a study of four various configurations of a high-speed compressor stage, a multistage compressor rig, and an axi-centrifugal engine test, this algorithm, using only a single pressure sensor, has consistently predicted the onset of rotating stall.

  17. Potential MiRNAs recognition site identification in 3' UTR regions by DSP methods.

    Science.gov (United States)

    Maggi, Norbert; Arrigo, Patrizio; Ruggiero, Carmelina

    2012-01-01

    MicroRNAs (miRNAs) are small non-coding RNAs that regulate fundamental cellular processes in diverse organisms and that have an important function in gene expression regulation. miRNAs seem capable to concurrently modulate hundreds of target genes. Their abnormal expression is emerging as important element in many pathological conditions. The identification of microRNA binding sites on those proteins that can be disease biomarker is fundamental to design synthetic artificial oligomers. In this paper we suggest a method, based on signal processing, to filter out potential miRNA recognition sites in the 3' UTR region of mRNAs.

  18. System Identification Method for Evaluating the Effect of Thickness Error on Backcalculated Pavement Layer Moduli

    Institute of Scientific and Technical Information of China (English)

    ZHONG Yan-hui; WANG Fu-ming; ZHANG Bei; CAI Ying-chun

    2004-01-01

    Based on system identification theory and FWD testing data, the effect of thickness error on backcalculating pavement layer moduli is studied and the method of singular value decomposition (SVD) is presented to solve the morbidity problem of sensitivity matrix in this paper.The results show that the thickness error has great effects on the backcalculated pavement layer moduli. The error of backcalculated moduli can be controlled within the range of ±15% by limiting the thickness error within the range of ±5%.

  19. Investigation of the stochastic subspace identification method for on-line wind turbine tower monitoring

    Science.gov (United States)

    Dai, Kaoshan; Wang, Ying; Lu, Wensheng; Ren, Xiaosong; Huang, Zhenhua

    2017-04-01

    Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

  20. Simple methods for the qualitative identification and quantitative determination of macrolide antibiotics.

    Science.gov (United States)

    Danielson, N D; Holeman, J A; Bristol, D C; Kirzner, D H

    1993-02-01

    Pyrolysis-gas chromatography is shown to be a rapid straightforward method for the qualitative differentiation of the macrolide antibiotics erythromycin, oleandomycin, troleandomycin, spiramycin and tylosin. Organic salts do not interfere and identification of erythromycin and troleandomycin in commercial products is viable. Spectrophotometric quantitation of these same five antibiotics after reaction with concentrated sulphuric acid is studied at about 470 nm. Reaction conditions such as acid concentration, time and temperature are provided. The sugar moieties of the antibiotics are proposed as the reactive sites. Detection limits are about 0.2-1.0 microg ml-1 [corrected] and analysis of pharmaceutical products should be possible.