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Sample records for integrative ortholog prediction

  1. An integrative approach to ortholog prediction for disease-focused and other functional studies

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    Perrimon Norbert

    2011-08-01

    Full Text Available Abstract Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt, for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM and genes in genome-wide association study (GWAS data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist. Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  2. An integrative approach to ortholog prediction for disease-focused and other functional studies.

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    Hu, Yanhui; Flockhart, Ian; Vinayagam, Arunachalam; Bergwitz, Clemens; Berger, Bonnie; Perrimon, Norbert; Mohr, Stephanie E

    2011-08-31

    Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  3. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

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    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  4. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

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    George L Sutphin

    2016-11-01

    Full Text Available The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

  5. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

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    Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

    2016-01-01

    The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

  6. Orthology prediction at scalable resolution by phylogenetic tree analysis

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    Huynen Martijn A

    2007-03-01

    Full Text Available Abstract Background Orthology is one of the cornerstones of gene function prediction. Dividing the phylogenetic relations between genes into either orthologs or paralogs is however an oversimplification. Already in two-species gene-phylogenies, the complicated, non-transitive nature of phylogenetic relations results in inparalogs and outparalogs. For situations with more than two species we lack semantics to specifically describe the phylogenetic relations, let alone to exploit them. Published procedures to extract orthologous groups from phylogenetic trees do not allow identification of orthology at various levels of resolution, nor do they document the relations between the orthologous groups. Results We introduce "levels of orthology" to describe the multi-level nature of gene relations. This is implemented in a program LOFT (Levels of Orthology From Trees that assigns hierarchical orthology numbers to genes based on a phylogenetic tree. To decide upon speciation and gene duplication events in a tree LOFT can be instructed either to perform classical species-tree reconciliation or to use the species overlap between partitions in the tree. The hierarchical orthology numbers assigned by LOFT effectively summarize the phylogenetic relations between genes. The resulting high-resolution orthologous groups are depicted in colour, facilitating visual inspection of (large trees. A benchmark for orthology prediction, that takes into account the varying levels of orthology between genes, shows that the phylogeny-based high-resolution orthology assignments made by LOFT are reliable. Conclusion The "levels of orthology" concept offers high resolution, reliable orthology, while preserving the relations between orthologous groups. A Windows as well as a preliminary Java version of LOFT is available from the LOFT website http://www.cmbi.ru.nl/LOFT.

  7. Orthology prediction at scalable resolution by phylogenetic tree analysis

    NARCIS (Netherlands)

    Heijden, R.T.J.M. van der; Snel, B.; Noort, V. van; Huynen, M.A.

    2007-01-01

    BACKGROUND: Orthology is one of the cornerstones of gene function prediction. Dividing the phylogenetic relations between genes into either orthologs or paralogs is however an oversimplification. Already in two-species gene-phylogenies, the complicated, non-transitive nature of phylogenetic

  8. Evaluating ortholog prediction algorithms in a yeast model clade.

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    Leonidas Salichos

    Full Text Available BACKGROUND: Accurate identification of orthologs is crucial for evolutionary studies and for functional annotation. Several algorithms have been developed for ortholog delineation, but so far, manually curated genome-scale biological databases of orthologous genes for algorithm evaluation have been lacking. We evaluated four popular ortholog prediction algorithms (MultiParanoid; and OrthoMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms cRBH and cRSD, respectively, so that they can predict orthologs across multiple taxa against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser. RESULTS: Examination of sensitivity [TP/(TP+FN], specificity [TN/(TN+FP], and accuracy [(TP+TN/(TP+TN+FP+FN] across a broad parameter range showed that cRBH was the most accurate and specific algorithm, whereas OrthoMCL was the most sensitive. Evaluation of the algorithms across a varying number of species showed that cRBH had the highest accuracy and lowest false discovery rate [FP/(FP+TP], followed by cRSD. Of the six species in our set, three descended from an ancestor that underwent whole genome duplication. Subsequent differential duplicate loss events in the three descendants resulted in distinct classes of gene loss patterns, including cases where the genes retained in the three descendants are paralogs, constituting 'traps' for ortholog prediction algorithms. We found that the false discovery rate of all algorithms dramatically increased in these traps. CONCLUSIONS: These results suggest that simple algorithms, like cRBH, may be better ortholog predictors than more complex ones (e.g., OrthoMCL and MultiParanoid for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics, but that all algorithms fail to accurately predict orthologs when paralogy

  9. Orthology prediction methods: a quality assessment using curated protein families.

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    Trachana, Kalliopi; Larsson, Tomas A; Powell, Sean; Chen, Wei-Hua; Doerks, Tobias; Muller, Jean; Bork, Peer

    2011-10-01

    The increasing number of sequenced genomes has prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of predictions and to explore biases caused by biological and technical factors are therefore required. We used 70 manually curated families to analyze the performance of five public methods in Metazoa. We analyzed the strengths and weaknesses of the methods and quantified the impact of biological and technical challenges. From the latter part of the analysis, genome annotation emerged as the largest single influencer, affecting up to 30% of the performance. Generally, most methods did well in assigning orthologous group but they failed to assign the exact number of genes for half of the groups. The publicly available benchmark set (http://eggnog.embl.de/orthobench/) should facilitate the improvement of current orthology assignment protocols, which is of utmost importance for many fields of biology and should be tackled by a broad scientific community. Copyright © 2011 WILEY Periodicals, Inc.

  10. Ortholog prediction of the Aspergillus genus applicable for synthetic biology

    DEFF Research Database (Denmark)

    Rasmussen, Jane Lind Nybo; Vesth, Tammi Camilla; Theobald, Sebastian

    of genotype-to-phenotype. To achieve this, we have developed orthologous protein prediction software that utilizes genus-wide genetic diversity. The approach is optimized for large data sets, based on BLASTp considering protein identity and alignment coverage, and clustering using single linkage of bi......The Aspergillus genus contains leading industrial microorganisms, excelling in producing bioactive compounds and enzymes. Using synthetic biology and bioinformatics, we aim to re-engineer these organisms for applications within human health, pharmaceuticals, environmental engineering, and food......-directional hits. The result is orthologous protein families describing the genomic and functional features of individual species, clades and the core/pan genome of Aspergillus; and applicable to genotype-to-phenotype analyses in other microbial genera....

  11. Construction of an ortholog database using the semantic web technology for integrative analysis of genomic data.

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    Chiba, Hirokazu; Nishide, Hiroyo; Uchiyama, Ikuo

    2015-01-01

    Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis.

  12. SPOCS: Software for Predicting and Visualizing Orthology/Paralogy Relationships Among Genomes

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    Curtis, Darren S.; Phillips, Aaron R.; Callister, Stephen J.; Conlan, Sean; McCue, Lee Ann

    2013-10-15

    At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid. AVAILABILITY AND IMPLEMENTATION: A SPOCS web application is freely available at http://cbb.pnnl.gov/portal/tools/spocs.html. Source code for Linux systems is also freely available under an open source license at http://cbb.pnnl.gov/portal/software/spocs.html; the Boost C++ libraries and BLAST are required.

  13. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

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    Hall Ross S

    2010-04-01

    Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

  14. Expression Pattern Similarities Support the Prediction of Orthologs Retaining Common Functions after Gene Duplication Events1[OPEN

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    Haberer, Georg; Panda, Arup; Das Laha, Shayani; Ghosh, Tapas Chandra; Schäffner, Anton R.

    2016-01-01

    The identification of functionally equivalent, orthologous genes (functional orthologs) across genomes is necessary for accurate transfer of experimental knowledge from well-characterized organisms to others. This frequently relies on automated, coding sequence-based approaches such as OrthoMCL, Inparanoid, and KOG, which usually work well for one-to-one homologous states. However, this strategy does not reliably work for plants due to the occurrence of extensive gene/genome duplication. Frequently, for one query gene, multiple orthologous genes are predicted in the other genome, and it is not clear a priori from sequence comparison and similarity which one preserves the ancestral function. We have studied 11 organ-dependent and stress-induced gene expression patterns of 286 Arabidopsis lyrata duplicated gene groups and compared them with the respective Arabidopsis (Arabidopsis thaliana) genes to predict putative expressologs and nonexpressologs based on gene expression similarity. Promoter sequence divergence as an additional tool to substantiate functional orthology only partially overlapped with expressolog classification. By cloning eight A. lyrata homologs and complementing them in the respective four Arabidopsis loss-of-function mutants, we experimentally proved that predicted expressologs are indeed functional orthologs, while nonexpressologs or nonfunctionalized orthologs are not. Our study demonstrates that even a small set of gene expression data in addition to sequence homologies are instrumental in the assignment of functional orthologs in the presence of multiple orthologs. PMID:27303025

  15. The use of orthologous sequences to predict the impact of amino acid substitutions on protein function.

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    Nicholas J Marini

    2010-05-01

    Full Text Available Computational predictions of the functional impact of genetic variation play a critical role in human genetics research. For nonsynonymous coding variants, most prediction algorithms make use of patterns of amino acid substitutions observed among homologous proteins at a given site. In particular, substitutions observed in orthologous proteins from other species are often assumed to be tolerated in the human protein as well. We examined this assumption by evaluating a panel of nonsynonymous mutants of a prototypical human enzyme, methylenetetrahydrofolate reductase (MTHFR, in a yeast cell-based functional assay. As expected, substitutions in human MTHFR at sites that are well-conserved across distant orthologs result in an impaired enzyme, while substitutions present in recently diverged sequences (including a 9-site mutant that "resurrects" the human-macaque ancestor result in a functional enzyme. We also interrogated 30 sites with varying degrees of conservation by creating substitutions in the human enzyme that are accepted in at least one ortholog of MTHFR. Quite surprisingly, most of these substitutions were deleterious to the human enzyme. The results suggest that selective constraints vary between phylogenetic lineages such that inclusion of distant orthologs to infer selective pressures on the human enzyme may be misleading. We propose that homologous proteins are best used to reconstruct ancestral sequences and infer amino acid conservation among only direct lineal ancestors of a particular protein. We show that such an "ancestral site preservation" measure outperforms other prediction methods, not only in our selected set for MTHFR, but also in an exhaustive set of E. coli LacI mutants.

  16. Semantic integration of information about orthologs and diseases: the OGO system.

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    Miñarro-Gimenez, Jose Antonio; Egaña Aranguren, Mikel; Martínez Béjar, Rodrigo; Fernández-Breis, Jesualdo Tomás; Madrid, Marisa

    2011-12-01

    Semantic Web technologies like RDF and OWL are currently applied in life sciences to improve knowledge management by integrating disparate information. Many of the systems that perform such task, however, only offer a SPARQL query interface, which is difficult to use for life scientists. We present the OGO system, which consists of a knowledge base that integrates information of orthologous sequences and genetic diseases, providing an easy to use ontology-constrain driven query interface. Such interface allows the users to define SPARQL queries through a graphical process, therefore not requiring SPARQL expertise. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

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    Neto Armando

    2012-11-01

    Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

  18. Enhancing the prediction of protein pairings between interacting families using orthology information

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    Pazos Florencio

    2008-01-01

    Full Text Available Abstract Background It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other. The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. Results For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. Conclusion Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.

  19. Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

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    Mihail R Halachev

    Full Text Available Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server. Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available

  20. Structural implications of mutations in the pea SYM8 symbiosis gene, the DMI1 ortholog, encoding a predicted ion channel

    DEFF Research Database (Denmark)

    Edwards, Anne; Heckmann, Anne Birgitte Lau; Yousafzai, Faridoon

    2007-01-01

    the aspartate to valine and identified a missense mutation (changing alanine to valine adjacent to the aspartate residues) in this predicted filter region; both mutations caused a loss of function. We also identified a loss-of-function missense mutation (changing arginine to isoleucine) in a domain proposed...

  1. Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

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    Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

    2006-01-01

    Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

  2. Orthology detection combining clustering and synteny for very large datasets

    OpenAIRE

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K.; Prohaska, Sonja J.; Stadler, Peter F.

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the ...

  3. RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach

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    Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna

    2010-05-26

    RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.

  4. Domain architecture conservation in orthologs

    Science.gov (United States)

    2011-01-01

    Background As orthologous proteins are expected to retain function more often than other homologs, they are often used for functional annotation transfer between species. However, ortholog identification methods do not take into account changes in domain architecture, which are likely to modify a protein's function. By domain architecture we refer to the sequential arrangement of domains along a protein sequence. To assess the level of domain architecture conservation among orthologs, we carried out a large-scale study of such events between human and 40 other species spanning the entire evolutionary range. We designed a score to measure domain architecture similarity and used it to analyze differences in domain architecture conservation between orthologs and paralogs relative to the conservation of primary sequence. We also statistically characterized the extents of different types of domain swapping events across pairs of orthologs and paralogs. Results The analysis shows that orthologs exhibit greater domain architecture conservation than paralogous homologs, even when differences in average sequence divergence are compensated for, for homologs that have diverged beyond a certain threshold. We interpret this as an indication of a stronger selective pressure on orthologs than paralogs to retain the domain architecture required for the proteins to perform a specific function. In general, orthologs as well as the closest paralogous homologs have very similar domain architectures, even at large evolutionary separation. The most common domain architecture changes observed in both ortholog and paralog pairs involved insertion/deletion of new domains, while domain shuffling and segment duplication/deletion were very infrequent. Conclusions On the whole, our results support the hypothesis that function conservation between orthologs demands higher domain architecture conservation than other types of homologs, relative to primary sequence conservation. This supports the

  5. Domain similarity based orthology detection

    OpenAIRE

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-01-01

    Background Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationa...

  6. GAPIT: genome association and prediction integrated tool.

    Science.gov (United States)

    Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

    2012-09-15

    Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

  7. New Tools in Orthology Analysis: A Brief Review of Promising Perspectives.

    Science.gov (United States)

    Nichio, Bruno T L; Marchaukoski, Jeroniza Nunes; Raittz, Roberto Tadeu

    2017-01-01

    Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST "all-against-all" methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing

  8. New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

    Directory of Open Access Journals (Sweden)

    Bruno T. L. Nichio

    2017-10-01

    Full Text Available Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST “all-against-all” methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm; or proteinOrtho (which improves the accuracy of ortholog groups; or ReMark (tackling the integration of the pipeline to turn the entry process automatic; or OrthAgogue (using algorithms developed to

  9. QuartetS-DB: a large-scale orthology database for prokaryotes and eukaryotes inferred by evolutionary evidence

    Directory of Open Access Journals (Sweden)

    Yu Chenggang

    2012-06-01

    Full Text Available Abstract Background The concept of orthology is key to decoding evolutionary relationships among genes across different species using comparative genomics. QuartetS is a recently reported algorithm for large-scale orthology detection. Based on the well-established evolutionary principle that gene duplication events discriminate paralogous from orthologous genes, QuartetS has been shown to improve orthology detection accuracy while maintaining computational efficiency. Description QuartetS-DB is a new orthology database constructed using the QuartetS algorithm. The database provides orthology predictions among 1621 complete genomes (1365 bacterial, 92 archaeal, and 164 eukaryotic, covering more than seven million proteins and four million pairwise orthologs. It is a major source of orthologous groups, containing more than 300,000 groups of orthologous proteins and 236,000 corresponding gene trees. The database also provides over 500,000 groups of inparalogs. In addition to its size, a distinguishing feature of QuartetS-DB is the ability to allow users to select a cutoff value that modulates the balance between prediction accuracy and coverage of the retrieved pairwise orthologs. The database is accessible at https://applications.bioanalysis.org/quartetsdb. Conclusions QuartetS-DB is one of the largest orthology resources available to date. Because its orthology predictions are underpinned by evolutionary evidence obtained from sequenced genomes, we expect its accuracy to continue to increase in future releases as the genomes of additional species are sequenced.

  10. Orthology detection combining clustering and synteny for very large datasets.

    Science.gov (United States)

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K; Prohaska, Sonja J; Stadler, Peter F

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  11. Orthology detection combining clustering and synteny for very large datasets.

    Directory of Open Access Journals (Sweden)

    Marcus Lechner

    Full Text Available The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  12. Domain similarity based orthology detection.

    Science.gov (United States)

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-05-13

    Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

  13. Kalman-predictive-proportional-integral-derivative (KPPID)

    International Nuclear Information System (INIS)

    Fluerasu, A.; Sutton, M.

    2004-01-01

    With third generation synchrotron X-ray sources, it is possible to acquire detailed structural information about the system under study with time resolution orders of magnitude faster than was possible a few years ago. These advances have generated many new challenges for changing and controlling the state of the system on very short time scales, in a uniform and controlled manner. For our particular X-ray experiments on crystallization or order-disorder phase transitions in metallic alloys, we need to change the sample temperature by hundreds of degrees as fast as possible while avoiding over or under shooting. To achieve this, we designed and implemented a computer-controlled temperature tracking system which combines standard Proportional-Integral-Derivative (PID) feedback, thermal modeling and finite difference thermal calculations (feedforward), and Kalman filtering of the temperature readings in order to reduce the noise. The resulting Kalman-Predictive-Proportional-Integral-Derivative (KPPID) algorithm allows us to obtain accurate control, to minimize the response time and to avoid over/under shooting, even in systems with inherently noisy temperature readings and time delays. The KPPID temperature controller was successfully implemented at the Advanced Photon Source at Argonne National Laboratories and was used to perform coherent and time-resolved X-ray diffraction experiments.

  14. Standardized benchmarking in the quest for orthologs

    DEFF Research Database (Denmark)

    Altenhoff, Adrian M; Boeckmann, Brigitte; Capella-Gutierrez, Salvador

    2016-01-01

    Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall...

  15. Program integration of predictive maintenance with reliability centered maintenance

    International Nuclear Information System (INIS)

    Strong, D.K. Jr; Wray, D.M.

    1990-01-01

    This paper addresses improving the safety and reliability of power plants in a cost-effective manner by integrating the recently developed reliability centered maintenance techniques with the traditional predictive maintenance techniques of nuclear power plants. The topics of the paper include a description of reliability centered maintenance (RCM), enhancing RCM with predictive maintenance, predictive maintenance programs, condition monitoring techniques, performance test techniques, the mid-Atlantic Reliability Centered Maintenance Users Group, test guides and the benefits of shared guide development

  16. The other side of comparative genomics: genes with no orthologs between the cow and other mammalian species

    Directory of Open Access Journals (Sweden)

    Ajmone-Marsan Paolo

    2009-12-01

    Full Text Available Abstract Background With the rapid growth in the availability of genome sequence data, the automated identification of orthologous genes between species (orthologs is of fundamental importance to facilitate functional annotation and studies on comparative and evolutionary genomics. Genes with no apparent orthologs between the bovine and human genome may be responsible for major differences between the species, however, such genes are often neglected in functional genomics studies. Results A BLAST-based method was exploited to explore the current annotation and orthology predictions in Ensembl. Genes with no orthologs between the two genomes were classified into groups based on alignments, ontology, manual curation and publicly available information. Starting from a high quality and specific set of orthology predictions, as provided by Ensembl, hidden relationship between genes and genomes of different mammalian species were unveiled using a highly sensitive approach, based on sequence similarity and genomic comparison. Conclusions The analysis identified 3,801 bovine genes with no orthologs in human and 1010 human genes with no orthologs in cow, among which 411 and 43 genes, respectively, had no match at all in the other species. Most of the apparently non-orthologous genes may potentially have orthologs which were missed in the annotation process, despite having a high percentage of identity, because of differences in gene length and structure. The comparative analysis reported here identified gene variants, new genes and species-specific features and gave an overview of the other side of orthology which may help to improve the annotation of the bovine genome and the knowledge of structural differences between species.

  17. Predictive integrated modelling for ITER scenarios

    International Nuclear Information System (INIS)

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

    2005-01-01

    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  18. Assessment of orthologous splicing isoforms in human and mouse orthologous genes

    Directory of Open Access Journals (Sweden)

    Horner David S

    2010-10-01

    Full Text Available Abstract Background Recent discoveries have highlighted the fact that alternative splicing and alternative transcripts are the rule, rather than the exception, in metazoan genes. Since multiple transcript and protein variants expressed by the same gene are, by definition, structurally distinct and need not to be functionally equivalent, the concept of gene orthology should be extended to the transcript level in order to describe evolutionary relationships between structurally similar transcript variants. In other words, the identification of true orthology relationships between gene products now should progress beyond primary sequence and "splicing orthology", consisting in ancestrally shared exon-intron structures, is required to define orthologous isoforms at transcript level. Results As a starting step in this direction, in this work we performed a large scale human- mouse gene comparison with a twofold goal: first, to assess if and to which extent traditional gene annotations such as RefSeq capture genuine splicing orthology; second, to provide a more detailed annotation and quantification of true human-mouse orthologous transcripts defined as transcripts of orthologous genes exhibiting the same splicing patterns. Conclusions We observed an identical exon/intron structure for 32% of human and mouse orthologous genes. This figure increases to 87% using less stringent criteria for gene structure similarity, thus implying that for about 13% of the human RefSeq annotated genes (and about 25% of the corresponding transcripts we could not identify any mouse transcript showing sufficient similarity to be confidently assigned as a splicing ortholog. Our data suggest that current gene and transcript data may still be rather incomplete - with several splicing variants still unknown. The observation that alternative splicing produces large numbers of alternative transcripts and proteins, some of them conserved across species and others truly species

  19. UK Environmental Prediction - integration and evaluation at the convective scale

    Science.gov (United States)

    Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

    2016-04-01

    Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

  20. Orthology and paralogy constraints: satisfiability and consistency.

    Science.gov (United States)

    Lafond, Manuel; El-Mabrouk, Nadia

    2014-01-01

    A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer orthology and paralogy relations between genes of a given gene family  G. But is a given set  C of orthology/paralogy constraints possible, i.e., can they simultaneously co-exist in an evolutionary history for  G? While previous studies have focused on full sets of constraints, here we consider the general case where  C does not necessarily involve a constraint for each pair of genes. The problem is subdivided in two parts: (1) Is  C satisfiable, i.e. can we find an event-labeled gene tree G inducing  C? (2) Is there such a G which is consistent, i.e., such that all displayed triplet phylogenies are included in a species tree? Previous results on the Graph sandwich problem can be used to answer to (1), and we provide polynomial-time algorithms for satisfiability and consistency with a given species tree. We also describe a new polynomial-time algorithm for the case of consistency with an unknown species tree and full knowledge of pairwise orthology/paralogy relationships, as well as a branch-and-bound algorithm in the case when unknown relations are present. We show that our algorithms can be used in combination with ProteinOrtho, a sequence similarity-based orthology detection tool, to extract a set of robust orthology/paralogy relationships.

  1. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    Azadeh, A.; Maghsoudi, A.; Sohrabkhani, S.

    2009-01-01

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  2. Development of an Integrated Moisture Index for predicting species composition

    Science.gov (United States)

    Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

    1996-01-01

    A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

  3. Orthology and paralogy constraints: satisfiability and consistency

    OpenAIRE

    Lafond, Manuel; El-Mabrouk, Nadia

    2014-01-01

    Background A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer orthology and paralogy relations between genes of a given gene family   G . But is a given set   C of orthology/paralogy constraints possible, i.e., can they simultaneously co-exist in an evolutionary history for   G ? While previous studies have focused on full sets of constraints, here we consider the general case where   C does not necessarily involve a ...

  4. On calculating the probability of a set of orthologous sequences

    Directory of Open Access Journals (Sweden)

    Junfeng Liu

    2009-02-01

    Full Text Available Junfeng Liu1,2, Liang Chen3, Hongyu Zhao4, Dirk F Moore1,2, Yong Lin1,2, Weichung Joe Shih1,21Biometrics Division, The Cancer, Institute of New Jersey, New Brunswick, NJ, USA; 2Department of Biostatistics, School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, USA; 3Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA; 4Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USAAbstract: Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman–Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment.Keywords: evolution, Jukes–Cantor model, Monte Carlo integration, Needleman–Wunsch alignment, orthologous

  5. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....

  6. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  7. Integration of Predictive Display and Aircraft Flight Control System

    Directory of Open Access Journals (Sweden)

    Efremov A.V.

    2017-01-01

    Full Text Available The synthesis of predictive display information and direct lift control system are considered for the path control tracking tasks (in particular landing task. The both solutions are based on pilot-vehicle system analysis and requirements to provide the highest accuracy and lowest pilot workload. The investigation was carried out for cases with and without time delay in aircraft dynamics. The efficiency of the both ways for the flying qualities improvement and their integration is tested by ground based simulation.

  8. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  9. An integrated logistic formula for prediction of complications from radiosurgery

    International Nuclear Information System (INIS)

    Flickinger, J.C.

    1989-01-01

    An integrated logistic model for predicting the probability of complications when small volumes of tissue receive an inhomogeneous radiation dose is described. This model can be used with either an exponential or linear quadratic correction for dose per fraction and time. Both the exponential and linear quadratic versions of this integrated logistic formula provide reasonable estimates of the tolerance of brain to radiosurgical dose distributions where there are small volumes of brain receiving high radiation doses and larger volumes receiving lower doses. This makes it possible to predict the probability of complications from stereotactic radiosurgery, as well as combinations of fractionated large volume irradiation with a radiosurgical boost. Complication probabilities predicted for single fraction radiosurgery with the Leksell Gamma Unit using 4, 8, 14, and 18 mm diameter collimators as well as for whole brain irradiation combined with a radiosurgical boost are presented. The exponential and linear quadratic versions of the integrated logistic formula provide useful methods of calculating the probability of complications from radiosurgical treatment

  10. Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules.

    Directory of Open Access Journals (Sweden)

    Konda Leela Sarath Kumar

    Full Text Available Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with 'High' reliability scoring, DEREK (accuracy = 72.73% and CCR = 71.44% and TOPKAT (accuracy = 60.00% and CCR = 61.67%. Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%, the coverage was very low (only 10 out of 77 molecules were predicted reliably.Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

  11. Prediction of rotor blade-vortex interaction using Volterra integrals

    Energy Technology Data Exchange (ETDEWEB)

    Wong, A.; Nitzsche, F. [Carleton Univ., Dept. of Mechanical and Aerospace Engineering, Ottawa, Ontario (Canada)]. E-mail: Fred_Nitzsche@carleton.ca; Khalid, M. [National Research Council Canada, Inst. for Aerospace Research, Ottawa, Ontario (Canada)

    2004-07-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  12. Prediction of rotor blade-vortex interaction using Volterra integrals

    International Nuclear Information System (INIS)

    Wong, A.; Nitzsche, F.; Khalid, M.

    2004-01-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  13. A database of annotated tentative orthologs from crop abiotic stress transcripts.

    Science.gov (United States)

    Balaji, Jayashree; Crouch, Jonathan H; Petite, Prasad V N S; Hoisington, David A

    2006-10-07

    A minimal requirement to initiate a comparative genomics study on plant responses to abiotic stresses is a dataset of orthologous sequences. The availability of a large amount of sequence information, including those derived from stress cDNA libraries allow for the identification of stress related genes and orthologs associated with the stress response. Orthologous sequences serve as tools to explore genes and their relationships across species. For this purpose, ESTs from stress cDNA libraries across 16 crop species including 6 important cereal crops and 10 dicots were systematically collated and subjected to bioinformatics analysis such as clustering, grouping of tentative orthologous sets, identification of protein motifs/patterns in the predicted protein sequence, and annotation with stress conditions, tissue/library source and putative function. All data are available to the scientific community at http://intranet.icrisat.org/gt1/tog/homepage.htm. We believe that the availability of annotated plant abiotic stress ortholog sets will be a valuable resource for researchers studying the biology of environmental stresses in plant systems, molecular evolution and genomics.

  14. Departure Queue Prediction for Strategic and Tactical Surface Scheduler Integration

    Science.gov (United States)

    Zelinski, Shannon; Windhorst, Robert

    2016-01-01

    A departure metering concept to be demonstrated at Charlotte Douglas International Airport (CLT) will integrate strategic and tactical surface scheduling components to enable the respective collaborative decision making and improved efficiency benefits these two methods of scheduling provide. This study analyzes the effect of tactical scheduling on strategic scheduler predictability. Strategic queue predictions and target gate pushback times to achieve a desired queue length are compared between fast time simulations of CLT surface operations with and without tactical scheduling. The use of variable departure rates as a strategic scheduler input was shown to substantially improve queue predictions over static departure rates. With target queue length calibration, the strategic scheduler can be tuned to produce average delays within one minute of the tactical scheduler. However, root mean square differences between strategic and tactical delays were between 12 and 15 minutes due to the different methods the strategic and tactical schedulers use to predict takeoff times and generate gate pushback clearances. This demonstrates how difficult it is for the strategic scheduler to predict tactical scheduler assigned gate delays on an individual flight basis as the tactical scheduler adjusts departure sequence to accommodate arrival interactions. Strategic/tactical scheduler compatibility may be improved by providing more arrival information to the strategic scheduler and stabilizing tactical scheduler changes to runway sequence in response to arrivals.

  15. Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

    Science.gov (United States)

    Stapanian, Martin A.; Mack, John; Adams, Jean V.; Gara, Brian; Micacchion, Mick

    2013-01-01

    Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

  16. Predictive Solar-Integrated Commercial Building Load Control

    Energy Technology Data Exchange (ETDEWEB)

    Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)

    2017-01-31

    This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.

  17. Integrated predictive modelling simulations of burning plasma experiment designs

    International Nuclear Information System (INIS)

    Bateman, Glenn; Onjun, Thawatchai; Kritz, Arnold H

    2003-01-01

    Models for the height of the pedestal at the edge of H-mode plasmas (Onjun T et al 2002 Phys. Plasmas 9 5018) are used together with the Multi-Mode core transport model (Bateman G et al 1998 Phys. Plasmas 5 1793) in the BALDUR integrated predictive modelling code to predict the performance of the ITER (Aymar A et al 2002 Plasma Phys. Control. Fusion 44 519), FIRE (Meade D M et al 2001 Fusion Technol. 39 336), and IGNITOR (Coppi B et al 2001 Nucl. Fusion 41 1253) fusion reactor designs. The simulation protocol used in this paper is tested by comparing predicted temperature and density profiles against experimental data from 33 H-mode discharges in the JET (Rebut P H et al 1985 Nucl. Fusion 25 1011) and DIII-D (Luxon J L et al 1985 Fusion Technol. 8 441) tokamaks. The sensitivities of the predictions are evaluated for the burning plasma experimental designs by using variations of the pedestal temperature model that are one standard deviation above and below the standard model. Simulations of the fusion reactor designs are carried out for scans in which the plasma density and auxiliary heating power are varied

  18. Neighborhood Integration and Connectivity Predict Cognitive Performance and Decline

    Directory of Open Access Journals (Sweden)

    Amber Watts PhD

    2015-08-01

    Full Text Available Objective: Neighborhood characteristics may be important for promoting walking, but little research has focused on older adults, especially those with cognitive impairment. We evaluated the role of neighborhood characteristics on cognitive function and decline over a 2-year period adjusting for measures of walking. Method: In a study of 64 older adults with and without mild Alzheimer’s disease (AD, we evaluated neighborhood integration and connectivity using geographical information systems data and space syntax analysis. In multiple regression analyses, we used these characteristics to predict 2-year declines in factor analytically derived cognitive scores (attention, verbal memory, mental status adjusting for age, sex, education, and self-reported walking. Results : Neighborhood integration and connectivity predicted cognitive performance at baseline, and changes in cognitive performance over 2 years. The relationships between neighborhood characteristics and cognitive performance were not fully explained by self-reported walking. Discussion : Clearer definitions of specific neighborhood characteristics associated with walkability are needed to better understand the mechanisms by which neighborhoods may impact cognitive outcomes. These results have implications for measuring neighborhood characteristics, design and maintenance of living spaces, and interventions to increase walking among older adults. We offer suggestions for future research measuring neighborhood characteristics and cognitive function.

  19. OrthoDB v8: update of the hierarchical catalog of orthologs and the underlying free software.

    Science.gov (United States)

    Kriventseva, Evgenia V; Tegenfeldt, Fredrik; Petty, Tom J; Waterhouse, Robert M; Simão, Felipe A; Pozdnyakov, Igor A; Ioannidis, Panagiotis; Zdobnov, Evgeny M

    2015-01-01

    Orthology, refining the concept of homology, is the cornerstone of evolutionary comparative studies. With the ever-increasing availability of genomic data, inference of orthology has become instrumental for generating hypotheses about gene functions crucial to many studies. This update of the OrthoDB hierarchical catalog of orthologs (http://www.orthodb.org) covers 3027 complete genomes, including the most comprehensive set of 87 arthropods, 61 vertebrates, 227 fungi and 2627 bacteria (sampling the most complete and representative genomes from over 11,000 available). In addition to the most extensive integration of functional annotations from UniProt, InterPro, GO, OMIM, model organism phenotypes and COG functional categories, OrthoDB uniquely provides evolutionary annotations including rates of ortholog sequence divergence, copy-number profiles, sibling groups and gene architectures. We re-designed the entirety of the OrthoDB website from the underlying technology to the user interface, enabling the user to specify species of interest and to select the relevant orthology level by the NCBI taxonomy. The text searches allow use of complex logic with various identifiers of genes, proteins, domains, ontologies or annotation keywords and phrases. Gene copy-number profiles can also be queried. This release comes with the freely available underlying ortholog clustering pipeline (http://www.orthodb.org/software). © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Microgravity Disturbance Predictions in the Combustion Integrated Rack

    Science.gov (United States)

    Just, M.; Grodsinsky, Carlos M.

    2002-01-01

    This paper will focus on the approach used to characterize microgravity disturbances in the Combustion Integrated Rack (CIR), currently scheduled for launch to the International Space Station (ISS) in 2005. Microgravity experiments contained within the CIR are extremely sensitive to vibratory and transient disturbances originating on-board and off-board the rack. Therefore, several techniques are implemented to isolate the critical science locations from external vibration. A combined testing and analysis approach is utilized to predict the resulting microgravity levels at the critical science location. The major topics to be addressed are: 1) CIR Vibration Isolation Approaches, 2) Disturbance Sources and Characterization, 3) Microgravity Predictive Modeling, 4) Science Microgravity Requirements, 6) Microgravity Control, and 7) On-Orbit Disturbance Measurement. The CIR is using the Passive Rack Isolation System (PaRIS) to isolate the rack from offboard rack disturbances. By utilizing this system, CIR is connected to the U.S. Lab module structure by either 13 or 14 umbilical lines and 8 spring / damper isolators. Some on-board CIR disturbers are locally isolated by grommets or wire ropes. CIR's environmental and science on board support equipment such as air circulation fans, pumps, water flow, air flow, solenoid valves, and computer hard drives cause disturbances within the rack. These disturbers along with the rack structure must be characterized to predict whether the on-orbit vibration levels during experimentation exceed the specified science microgravity vibration level requirements. Both vibratory and transient disturbance conditions are addressed. Disturbance levels/analytical inputs are obtained for each individual disturber in a "free floating" condition in the Glenn Research Center (GRC) Microgravity Emissions Lab (MEL). Flight spare hardware is tested on an Orbital Replacement Unit (ORU) basis. Based on test and analysis, maximum disturbance level

  1. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  2. IONS: Identification of Orthologs by Neighborhood and Similarity-an Automated Method to Identify Orthologs in Chromosomal Regions of Common Evolutionary Ancestry and its Application to Hemiascomycetous Yeasts.

    Science.gov (United States)

    Seret, Marie-Line; Baret, Philippe V

    2011-01-01

    Comparative sequence analysis is widely used to infer gene function and study genome evolution and requires proper ortholog identification across different genomes. We have developed a program for the Identification of Orthologs in one-to-one relationship by Neighborhood and Similarity (IONS) between closely related species. The algorithm combines two levels of evidence to determine co-ancestrality at the genome scale: sequence similarity and shared neighborhood. The method was initially designed to provide anchor points for syntenic blocks within the Génolevures project concerning nine hemiascomycetous yeasts (about 50,000 genes) and is applicable to different input databases. Comparison based on use of a Rand index shows that the results are highly consistent with the pillars of the Yeast Gene Order Browser, a manually curated database. Compared with SYNERGY, another algorithm reporting homology relationships, our method's main advantages are its automation and the absence of dataset-dependent parameters, facilitating consistent integration of newly released genomes.

  3. An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.

    Science.gov (United States)

    Liu, Bingqiang; Zhang, Hanyuan; Zhou, Chuan; Li, Guojun; Fennell, Anne; Wang, Guanghui; Kang, Yu; Liu, Qi; Ma, Qin

    2016-08-09

    Phylogenetic footprinting is an important computational technique for identifying cis-regulatory motifs in orthologous regulatory regions from multiple genomes, as motifs tend to evolve slower than their surrounding non-functional sequences. Its application, however, has several difficulties for optimizing the selection of orthologous data and reducing the false positives in motif prediction. Here we present an integrative phylogenetic footprinting framework for accurate motif predictions in prokaryotic genomes (MP(3)). The framework includes a new orthologous data preparation procedure, an additional promoter scoring and pruning method and an integration of six existing motif finding algorithms as basic motif search engines. Specifically, we collected orthologous genes from available prokaryotic genomes and built the orthologous regulatory regions based on sequence similarity of promoter regions. This procedure made full use of the large-scale genomic data and taxonomy information and filtered out the promoters with limited contribution to produce a high quality orthologous promoter set. The promoter scoring and pruning is implemented through motif voting by a set of complementary predicting tools that mine as many motif candidates as possible and simultaneously eliminate the effect of random noise. We have applied the framework to Escherichia coli k12 genome and evaluated the prediction performance through comparison with seven existing programs. This evaluation was systematically carried out at the nucleotide and binding site level, and the results showed that MP(3) consistently outperformed other popular motif finding tools. We have integrated MP(3) into our motif identification and analysis server DMINDA, allowing users to efficiently identify and analyze motifs in 2,072 completely sequenced prokaryotic genomes. The performance evaluation indicated that MP(3) is effective for predicting regulatory motifs in prokaryotic genomes. Its application may enhance

  4. Frontoparietal white matter integrity predicts haptic performance in chronic stroke

    Directory of Open Access Journals (Sweden)

    Alexandra L. Borstad

    2016-01-01

    . Age strongly correlated with the shared variance across tracts in the control, but not in the poststroke participants. A moderate to good relationship was found between ipsilesional T–M1 MD and affected hand HASTe score (r = −0.62, p = 0.006 and less affected hand HASTe score (r = −0.53, p = 0.022. Regression analysis revealed approximately 90% of the variance in affected hand HASTe score was predicted by the white matter integrity in the frontoparietal network (as indexed by MD in poststroke participants while 87% of the variance in HASTe score was predicted in control participants. This study demonstrates the importance of frontoparietal white matter in mediating haptic performance and specifically identifies that T–M1 and precuneus interhemispheric tracts may be appropriate targets for piloting rehabilitation interventions, such as noninvasive brain stimulation, when the goal is to improve poststroke haptic performance.

  5. Frontoparietal white matter integrity predicts haptic performance in chronic stroke.

    Science.gov (United States)

    Borstad, Alexandra L; Choi, Seongjin; Schmalbrock, Petra; Nichols-Larsen, Deborah S

    2016-01-01

    strongly correlated with the shared variance across tracts in the control, but not in the poststroke participants. A moderate to good relationship was found between ipsilesional T-M1 MD and affected hand HASTe score (r = - 0.62, p = 0.006) and less affected hand HASTe score (r = - 0.53, p = 0.022). Regression analysis revealed approximately 90% of the variance in affected hand HASTe score was predicted by the white matter integrity in the frontoparietal network (as indexed by MD) in poststroke participants while 87% of the variance in HASTe score was predicted in control participants. This study demonstrates the importance of frontoparietal white matter in mediating haptic performance and specifically identifies that T-M1 and precuneus interhemispheric tracts may be appropriate targets for piloting rehabilitation interventions, such as noninvasive brain stimulation, when the goal is to improve poststroke haptic performance.

  6. Phylogenetic reconstruction of orthology, paralogy, and conserved synteny for dog and human.

    Science.gov (United States)

    Goodstadt, Leo; Ponting, Chris P

    2006-09-29

    Accurate predictions of orthology and paralogy relationships are necessary to infer human molecular function from experiments in model organisms. Previous genome-scale approaches to predicting these relationships have been limited by their use of protein similarity and their failure to take into account multiple splicing events and gene prediction errors. We have developed PhyOP, a new phylogenetic orthology prediction pipeline based on synonymous rate estimates, which accurately predicts orthology and paralogy relationships for transcripts, genes, exons, or genomic segments between closely related genomes. We were able to identify orthologue relationships to human genes for 93% of all dog genes from Ensembl. Among 1:1 orthologues, the alignments covered a median of 97.4% of protein sequences, and 92% of orthologues shared essentially identical gene structures. PhyOP accurately recapitulated genomic maps of conserved synteny. Benchmarking against predictions from Ensembl and Inparanoid showed that PhyOP is more accurate, especially in its predictions of paralogy. Nearly half (46%) of PhyOP paralogy predictions are unique. Using PhyOP to investigate orthologues and paralogues in the human and dog genomes, we found that the human assembly contains 3-fold more gene duplications than the dog. Species-specific duplicate genes, or "in-paralogues," are generally shorter and have fewer exons than 1:1 orthologues, which is consistent with selective constraints and mutation biases based on the sizes of duplicated genes. In-paralogues have experienced elevated amino acid and synonymous nucleotide substitution rates. Duplicates possess similar biological functions for either the dog or human lineages. Having accounted for 2,954 likely pseudogenes and gene fragments, and after separating 346 erroneously merged genes, we estimated that the human genome encodes a minimum of 19,700 protein-coding genes, similar to the gene count of nematode worms. PhyOP is a fast and robust

  7. Phylogenetic reconstruction of orthology, paralogy, and conserved synteny for dog and human.

    Directory of Open Access Journals (Sweden)

    Leo Goodstadt

    2006-09-01

    Full Text Available Accurate predictions of orthology and paralogy relationships are necessary to infer human molecular function from experiments in model organisms. Previous genome-scale approaches to predicting these relationships have been limited by their use of protein similarity and their failure to take into account multiple splicing events and gene prediction errors. We have developed PhyOP, a new phylogenetic orthology prediction pipeline based on synonymous rate estimates, which accurately predicts orthology and paralogy relationships for transcripts, genes, exons, or genomic segments between closely related genomes. We were able to identify orthologue relationships to human genes for 93% of all dog genes from Ensembl. Among 1:1 orthologues, the alignments covered a median of 97.4% of protein sequences, and 92% of orthologues shared essentially identical gene structures. PhyOP accurately recapitulated genomic maps of conserved synteny. Benchmarking against predictions from Ensembl and Inparanoid showed that PhyOP is more accurate, especially in its predictions of paralogy. Nearly half (46% of PhyOP paralogy predictions are unique. Using PhyOP to investigate orthologues and paralogues in the human and dog genomes, we found that the human assembly contains 3-fold more gene duplications than the dog. Species-specific duplicate genes, or "in-paralogues," are generally shorter and have fewer exons than 1:1 orthologues, which is consistent with selective constraints and mutation biases based on the sizes of duplicated genes. In-paralogues have experienced elevated amino acid and synonymous nucleotide substitution rates. Duplicates possess similar biological functions for either the dog or human lineages. Having accounted for 2,954 likely pseudogenes and gene fragments, and after separating 346 erroneously merged genes, we estimated that the human genome encodes a minimum of 19,700 protein-coding genes, similar to the gene count of nematode worms. PhyOP is a

  8. Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

    Science.gov (United States)

    Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi

    2016-04-01

    It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in

  9. Orthology Guided Assembly in highly heterozygous crops

    DEFF Research Database (Denmark)

    Ruttink, Tom; Sterck, Lieven; Rohde, Antje

    2013-01-01

    to outbreeding crop species hamper De Bruijn Graph-based de novo assembly algorithms, causing transcript fragmentation and the redundant assembly of allelic contigs. If multiple genotypes are sequenced to study genetic diversity, primary de novo assembly is best performed per genotype to limit the level......Despite current advances in next-generation sequencing data analysis procedures, de novo assembly of a reference sequence required for SNP discovery and expression analysis is still a major challenge in genetically uncharacterized, highly heterozygous species. High levels of polymorphism inherent...... of polymorphism and avoid transcript fragmentation. Here, we propose an Orthology Guided Assembly procedure that first uses sequence similarity (tBLASTn) to proteins of a model species to select allelic and fragmented contigs from all genotypes and then performs CAP3 clustering on a gene-by-gene basis. Thus, we...

  10. ORCAN-a web-based meta-server for real-time detection and functional annotation of orthologs.

    Science.gov (United States)

    Zielezinski, Andrzej; Dziubek, Michal; Sliski, Jan; Karlowski, Wojciech M

    2017-04-15

    ORCAN (ORtholog sCANner) is a web-based meta-server for one-click evolutionary and functional annotation of protein sequences. The server combines information from the most popular orthology-prediction resources, including four tools and four online databases. Functional annotation utilizes five additional comparisons between the query and identified homologs, including: sequence similarity, protein domain architectures, functional motifs, Gene Ontology term assignments and a list of associated articles. Furthermore, the server uses a plurality-based rating system to evaluate the orthology relationships and to rank the reference proteins by their evolutionary and functional relevance to the query. Using a dataset of ∼1 million true yeast orthologs as a sample reference set, we show that combining multiple orthology-prediction tools in ORCAN increases the sensitivity and precision by 1-2 percent points. The service is available for free at http://www.combio.pl/orcan/ . wmk@amu.edu.pl. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  11. New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

    OpenAIRE

    Bruno T. L. Nichio; Jeroniza Nunes Marchaukoski; Roberto Tadeu Raittz

    2017-01-01

    Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orth...

  12. Protein (Viridiplantae): 159470305 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available predicted protein Chlamydomonas reinhardtii MSSRPKRAASANMANVIAAEKANKAAALHAWPKMWATKLEAQLQLMFMPTRLHRRPLHQGTCRNYSTAPGITGVIELTSAFYRMYPNATFVFNKETAAKGTYRGEEETAASWWLKHVGSKLEIYLSPLRCRPEVSR ...

  13. Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

    Science.gov (United States)

    Phan, John H; Hoffman, Ryan; Kothari, Sonal; Wu, Po-Yen; Wang, May D

    2016-02-01

    The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

  14. Accurate Holdup Calculations with Predictive Modeling & Data Integration

    Energy Technology Data Exchange (ETDEWEB)

    Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering

    2017-04-03

    Bayes’ Theorem, one must have a model y(x) that maps the state variables x (the solution in this case) to the measurements y. In this case, the unknown state variables are the configuration and composition of the heldup SNM. The measurements are the detector readings. Thus, the natural model is neutral-particle radiation transport where a wealth of computational tools exists for performing these simulations accurately and efficiently. The combination of predictive model and Bayesian inference forms the Data Integration with Modeled Predictions (DIMP) method that serves as foundation for this project. The cost functional describing the model-to-data misfit is computed via a norm created by the inverse of the covariance matrix of the model parameters and responses. Since the model y(x) for the holdup problem is nonlinear, a nonlinear optimization on Q is conducted via Newton-type iterative methods to find the optimal values of the model parameters x. This project comprised a collaboration between NC State University (NCSU), the University of South Carolina (USC), and Oak Ridge National Laboratory (ORNL). The project was originally proposed in seven main tasks with an eighth contingency task to be performed if time and funding permitted; in fact time did not permit commencement of the contingency task and it was not performed. The remaining tasks involved holdup analysis with gamma detection strategies and separately with neutrons based on coincidence counting. Early in the project, and upon consultation with experts in coincidence counting it became evident that this approach is not viable for holdup applications and this task was replaced with an alternative, but valuable investigation that was carried out by the USC partner. Nevertheless, the experimental 4 measurements at ORNL of both gamma and neutron sources for the purpose of constructing Detector Response Functions (DRFs) with the associated uncertainties were indeed completed.

  15. Predicting freshwater habitat integrity using land-use surrogates

    African Journals Online (AJOL)

    2007-04-02

    Apr 2, 2007 ... Quantification of potential surrogates of freshwater habitat integrity. We chose a series of land-use variables that might be suitable predictors for assessing freshwater habitat integrity from the land cover map (CSIR 2005) and added separate GIS surfaces for human population density and the distribution of ...

  16. Context mining and integration into predictive web analytics

    NARCIS (Netherlands)

    Kiseleva, Y.

    2013-01-01

    Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or

  17. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

    Science.gov (United States)

    Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

    2011-07-01

    The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

  18. Increased taxon sampling reveals thousands of hidden orthologs in flatworms

    Science.gov (United States)

    2017-01-01

    Gains and losses shape the gene complement of animal lineages and are a fundamental aspect of genomic evolution. Acquiring a comprehensive view of the evolution of gene repertoires is limited by the intrinsic limitations of common sequence similarity searches and available databases. Thus, a subset of the gene complement of an organism consists of hidden orthologs, i.e., those with no apparent homology to sequenced animal lineages—mistakenly considered new genes—but actually representing rapidly evolving orthologs or undetected paralogs. Here, we describe Leapfrog, a simple automated BLAST pipeline that leverages increased taxon sampling to overcome long evolutionary distances and identify putative hidden orthologs in large transcriptomic databases by transitive homology. As a case study, we used 35 transcriptomes of 29 flatworm lineages to recover 3427 putative hidden orthologs, some unidentified by OrthoFinder and HaMStR, two common orthogroup inference algorithms. Unexpectedly, we do not observe a correlation between the number of putative hidden orthologs in a lineage and its “average” evolutionary rate. Hidden orthologs do not show unusual sequence composition biases that might account for systematic errors in sequence similarity searches. Instead, gene duplication with divergence of one paralog and weak positive selection appear to underlie hidden orthology in Platyhelminthes. By using Leapfrog, we identify key centrosome-related genes and homeodomain classes previously reported as absent in free-living flatworms, e.g., planarians. Altogether, our findings demonstrate that hidden orthologs comprise a significant proportion of the gene repertoire in flatworms, qualifying the impact of gene losses and gains in gene complement evolution. PMID:28400424

  19. Protein (Viridiplantae): 145354532 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 436017:4420 predicted protein Ostreococcus lucimarinus CCE9901 MSTRRPTTRARADDGFARDDDEDDGAHDDVAANTIVVYTKPGCCLCDGLKDKLDAAVDAAARAPPGASL...ECLRDFALCVRDVSTNAAWAESYAGSVPRVFVRVAVDAASTERSSVVSREFARPPPKRAAARVAEDLASLVRRACAPARAGWTVVTTTAWDAPSSSF ...

  20. Protein (Viridiplantae): 224169381 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 5297 3694:5297 predicted protein Populus trichocarpa MMINVVFAADSGLGSDAVFAADAEIGSDAVFAADSGLGSDAVFAADAEIGSDAVFAADSGLGSDAVFAADSGLVFAA...DSGLGNDAVFAADSGLGSDAVFAADAEIDSDAVFAADSGMGSDAAFAADSGLGSDAVFAADAEISSDAVFAADSGLGSDAVFAA...HFLIGSDAVFAADAEIGSDAVFAADAEIGSDAVFAADFSMNSDAELGGRGKTDFR ...

  1. Conserved repertoire of orthologous vomeronasal type 1 receptor genes in ruminant species

    Directory of Open Access Journals (Sweden)

    Okamura Hiroaki

    2009-09-01

    Full Text Available Abstract Background In mammals, pheromones play an important role in social and innate reproductive behavior within species. In rodents, vomeronasal receptor type 1 (V1R, which is specifically expressed in the vomeronasal organ, is thought to detect pheromones. The V1R gene repertoire differs dramatically between mammalian species, and the presence of species-specific V1R subfamilies in mouse and rat suggests that V1R plays a profound role in species-specific recognition of pheromones. In ruminants, however, the molecular mechanism(s for pheromone perception is not well understood. Interestingly, goat male pheromone, which can induce out-of-season ovulation in anestrous females, causes the same pheromone response in sheep, and vice versa, suggesting that there may be mechanisms for detecting "inter-species" pheromones among ruminant species. Results We isolated 23 goat and 21 sheep intact V1R genes based on sequence similarity with 32 cow V1R genes in the cow genome database. We found that all of the goat and sheep V1R genes have orthologs in their cross-species counterparts among these three ruminant species and that the sequence identity of V1R orthologous pairs among these ruminants is much higher than that of mouse-rat V1R orthologous pairs. Furthermore, all goat V1Rs examined thus far are expressed not only in the vomeronasal organ but also in the main olfactory epithelium. Conclusion Our results suggest that, compared with rodents, the repertoire of orthologous V1R genes is remarkably conserved among the ruminants cow, sheep and goat. We predict that these orthologous V1Rs can detect the same or closely related chemical compound(s within each orthologous set/pair. Furthermore, all identified goat V1Rs are expressed in the vomeronasal organ and the main olfactory epithelium, suggesting that V1R-mediated ligand information can be detected and processed by both the main and accessory olfactory systems. The fact that ruminant and rodent V1Rs

  2. Evaluating and Predicting Patient Safety for Medical Devices With Integral Information Technology

    Science.gov (United States)

    2005-01-01

    323 Evaluating and Predicting Patient Safety for Medical Devices with Integral Information Technology Jiajie Zhang, Vimla L. Patel, Todd R...errors are due to inappropriate designs for user interactions, rather than mechanical failures. Evaluating and predicting patient safety in medical ...the users on the identified trouble spots in the devices. We developed two methods for evaluating and predicting patient safety in medical devices

  3. Predicting freshwater habitat integrity using land-use surrogates

    CSIR Research Space (South Africa)

    Amis, MA

    2007-04-01

    Full Text Available Freshwater biodiversity is globally threatened due to human disturbances, but freshwater ecosystems have been accorded less protection than their terrestrial and marine counterparts. Few criteria exist for assessing the habitat integrity of rivers...

  4. Protein (Viridiplantae): 203761 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 017 ... 38832:3226 ... 38833:3539 ... 564608:3539 predicted protein Micromonas pusilla CCMP1545 MHPPLTLENHPLCKDVVIALKRCHRDNPWARAWGACNEQKWALDDCLKKQKLFKFRANHAKAKAQQDRLRRRVEKYGHATTNGQFKG

  5. Protein (Viridiplantae): 224094212 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 5297 3694:5297 predicted protein Populus trichocarpa MMMNAVFAADAEIGSDAVFAADSGLGSDAVFAADFGLGSDAVFAADAEIGSDAVFAA...DSGLGSDTAFTTDTEIGSNTVFAAHFLIGSDAVFAADAEIGSDAVFAADFSMNSDAELGGRVFAADAEIGSDAVFAADSGLGSEAVFAADSGLGSDAVFAA...DAEIGSDAVFAADSGLGSDAVFAADSGLGSDAVFAADAEIGSDKVFAADSGLGSDAVFAADSGLGSHAVFAADAEIGSDAVFAADSGLGSDAVFAA...DSGLGSDAAFTTDTEIGSDTVFAAHFLIGSDAVFAADAEIGSDAVFAADFSMNSDAELGGRGKNWF ...

  6. Protein (Viridiplantae): 714399 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 3051:329 ... 3052:329 ... 3055:329 ... predicted protein Chlamydomonas reinhardtii MAPAALPGRSVKSKQAHLLRTDAHRVKSKQAHLLRTDAHRVKSKQAHLLRTDA...HRVKSKQAHLLRTDAHRVKSKQAHLLRTDAHRVALTTLTGALSLFGGACTATSFVLQVSASAASYAASLRLSCPAVPSLTDVA

  7. Pleurochrysome: A Web Database of Pleurochrysis Transcripts and Orthologs Among Heterogeneous Algae

    Science.gov (United States)

    Fujiwara, Shoko; Takatsuka, Yukiko; Hirokawa, Yasutaka; Tsuzuki, Mikio; Takano, Tomoyuki; Kobayashi, Masaaki; Suda, Kunihiro; Asamizu, Erika; Yokoyama, Koji; Shibata, Daisuke; Tabata, Satoshi; Yano, Kentaro

    2016-01-01

    Pleurochrysis is a coccolithophorid genus, which belongs to the Coccolithales in the Haptophyta. The genus has been used extensively for biological research, together with Emiliania in the Isochrysidales, to understand distinctive features between the two coccolithophorid-including orders. However, molecular biological research on Pleurochrysis such as elucidation of the molecular mechanism behind coccolith formation has not made great progress at least in part because of lack of comprehensive gene information. To provide such information to the research community, we built an open web database, the Pleurochrysome (http://bioinf.mind.meiji.ac.jp/phapt/), which currently stores 9,023 unique gene sequences (designated as UNIGENEs) assembled from expressed sequence tag sequences of P. haptonemofera as core information. The UNIGENEs were annotated with gene sequences sharing significant homology, conserved domains, Gene Ontology, KEGG Orthology, predicted subcellular localization, open reading frames and orthologous relationship with genes of 10 other algal species, a cyanobacterium and the yeast Saccharomyces cerevisiae. This sequence and annotation information can be easily accessed via several search functions. Besides fundamental functions such as BLAST and keyword searches, this database also offers search functions to explore orthologous genes in the 12 organisms and to seek novel genes. The Pleurochrysome will promote molecular biological and phylogenetic research on coccolithophorids and other haptophytes by helping scientists mine data from the primary transcriptome of P. haptonemofera. PMID:26746174

  8. Prediction of thermo-mechanical integrity of wafer backend processes

    NARCIS (Netherlands)

    Gonda, V.; Toonder, den J.M.J.; Beijer, J.G.J.; Zhang, G.Q.; Hoofman, R.J.O.M.; Ernst, L.J.; Ernst, L.J.

    2003-01-01

    More than 65% of IC failures are related to thermal and mechanical problems. For wafer backend processes, thermo-mechanical failure is one of the major bottlenecks. The ongoing technological trends like miniaturization, introduction of new materials, and function/product integration will increase

  9. Preliminary background prediction for the INTEGRAL x-ray monitor

    DEFF Research Database (Denmark)

    Feroci, M.; Costa, E.; Budtz-Joergensen, C.

    1996-01-01

    The JEM-X (joint European x-ray monitor) experiment will be flown onboard the ESA's INTEGRAL satellite. The instrumental background level of the two JEM-X twin detectors will depend on several parameters, among which the satellite orbit and mass distribution, and the detectors materials play...

  10. Integrating models to predict regional haze from wildland fire.

    Science.gov (United States)

    D. McKenzie; S.M. O' Neill; N. Larkin; R.A. Norheim

    2006-01-01

    Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling...

  11. Tide Gauge and Satellite Altimetry Integration for Storm Surge Prediction

    DEFF Research Database (Denmark)

    Andersen, Ole Baltazar; Cheng, Yongcun; Deng, X.

    2013-01-01

    of the Northeast Australia, we have investigated several large cyclones causing much destruction when they hit the coast. One of these being the Cyclone Larry, which hit the Queensland coast in March 2006 and caused both losses of lives as well as huge devastation. Here we demonstrate the importance of integrating...

  12. Statistical timing for parametric yield prediction of digital integrated circuits

    NARCIS (Netherlands)

    Jess, J.A.G.; Kalafala, K.; Naidu, S.R.; Otten, R.H.J.M.; Visweswariah, C.

    2006-01-01

    Uncertainty in circuit performance due to manufacturing and environmental variations is increasing with each new generation of technology. It is therefore important to predict the performance of a chip as a probabilistic quantity. This paper proposes three novel path-based algorithms for statistical

  13. Predicting phenology by integrating ecology, evolution and climate science

    Science.gov (United States)

    Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

    2011-01-01

    Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

  14. Developing a comprehensive training curriculum for integrated predictive maintenance

    Science.gov (United States)

    Wurzbach, Richard N.

    2002-03-01

    On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

  15. Edaphic history over seedling characters predicts integration and plasticity of integration across geologically variable populations of Arabidopsis thaliana.

    Science.gov (United States)

    Cousins, Elsa A; Murren, Courtney J

    2017-12-01

    Studies on phenotypic plasticity and plasticity of integration have uncovered functionally linked modules of aboveground traits and seedlings of Arabidopsis thaliana , but we lack details about belowground variation in adult plants. Functional modules can be comprised of additional suites of traits that respond to environmental variation. We assessed whether shoot and root responses to nutrient environments in adult A. thaliana were predictable from seedling traits or population-specific geologic soil characteristics at the site of origin. We compared 17 natural accessions from across the native range of A. thaliana using 14-day-old seedlings grown on agar or sand and plants grown to maturity across nutrient treatments in sand. We measured aboveground size, reproduction, timing traits, root length, and root diameter. Edaphic characteristics were obtained from a global-scale dataset and related to field data. We detected significant among-population variation in root traits of seedlings and adults and in plasticity in aboveground and belowground traits of adult plants. Phenotypic integration of roots and shoots varied by population and environment. Relative integration was greater in roots than in shoots, and integration was predicted by edaphic soil history, particularly organic carbon content, whereas seedling traits did not predict later ontogenetic stages. Soil environment of origin has significant effects on phenotypic plasticity in response to nutrients, and on phenotypic integration of root modules and shoot modules. Root traits varied among populations in reproductively mature individuals, indicating potential for adaptive and integrated functional responses of root systems in annuals. © 2017 Botanical Society of America.

  16. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

  17. Protein (Viridiplantae): 786990 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 79506 Solanum tuberosum MEETSTSSNNAKAKARVRVCITRKKTLKDKRAKLYIIRRCLYMLLCWKERAEFCNVGNRESTA ...3993 4070:3993 ... 424551:3993 ... 424574:3993 ... 4107:3993 ... 4113:2476 ... PREDICTED: uncharacterized protein LOC1025

  18. Protein (Viridiplantae): 788948 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 832:429 ... 296587:152 ... predicted protein Micromonas sp. RCC299 MRARGKVEVELQGVQRLSARCKECGGSQICEHGRQRFHCRECGGSGICEHGRGRHRCKECG...GSQICEHGRVRSQCKECGGSGICEHGRRRSLCKECGGSGICEHGRQRYSCKECGGAGICEHGRERYSCKECRAAKAGTFPDVDVEVGVTEDA...SSKGAKRKRAPYTKGPCEHGVKYRSQCKVCSACPHGRQRNKCKECGGASICVHGRERNKCKECGGASICEHGRQRSHCKECGGASICVHARERNKCKECG...GASFCEHGRQRRYCKECGGSQICEHGRVRRLCKECGGSGICEHGRQRPQCKECGGSQICEHGRQRYSCKECRAAKAKQR

  19. Protein (Viridiplantae): 788912 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 8832:429 ... 296587:152 ... predicted protein Micromonas sp. RCC299 MLPDVDVEVGVTEDASSKGTKRKRAPKTKGPCEHGVKRRSNCKVCSACPHGKWRYWCKECG...GAGICEHGRERRRCKECGGASICEHGRQRRYCKECGGGSICEHGRVRYYCKECGGSGICEHGRDRSRCKECGGGSICEHGRERYYCKECGGSQICEHGRRRSECKECG...GSQICEHGRRRSECKECGGSAICEHGRQRYYCKECGGSGICEHGRDRSRCKECGGGSICEHGRERYYCKECG...GAGICEHGRIRSTCKECGGSRICEHDRQRHTCKDCGGSQICEHGRVRSKCKECGGSGICEHGRHRQYCKECGGGSFCEHGRQRRKCKECGGSQI

  20. Protein (Viridiplantae): 788908 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 8832:429 ... 296587:152 ... predicted protein Micromonas sp. RCC299 MPAIWNVSGPLPDVDVEVGVTEDASSKGTKRKRAPPTKGPCEHG...VKPRSKCKVCSACPHGKRRSECKECGGSQICEHGRRRTQCKECGGSQICEHGRVRSTCKECGGSGLCEHGRERSRCKECGGPGICEHGRVRSRCKECGGSQICEHGRQRSKCKECG...GGSICEHGRIRSTCKECGGSQICEHGRERSKCKECGGGAICEHGRIRSTCKECGGGAICEHGRERHRCKECGGSGICEHGRRRSQCKECG...GSAICEHGRHRQYCKECGGGSICEHGRIRSTCKECGGGAICEHGRQRHRCKECGGASFCEHGRQRSRCKECGGSGICEHGRRRSTCKECRAAN

  1. Protein (Viridiplantae): 746637 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 218 ... 38832:274 ... 38833:414 ... 564608:414 predicted protein Micromonas pusilla CCMP1545 MAQLPSYEVDDGEDSMPGAPGEGPMTDSMQGPPVEVPTSDSM...PGAPGEVPTMDSMHGPPVEVPTMDSMHGAPVEVPMMDSMPGAPVEVPTMDSMQGPPVEVPTMDSMQGAPGEGPMTDSMHGAPGEGPTMDSMNSGNPTKCVVPDWCSTYPPEMQKSKPECQCPDDSHP

  2. Protein (Viridiplantae): 780231 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 401 ... 38832:1362 ... 38833:877 ... 564608:877 predicted protein Micromonas pusilla CCMP1545 MQDSMHDTMHDSIQDSMHDSIQDSMQDSM...AKEEEEPAEPPAKEEEAHAEPPAKEEEAHAEPPAKEEDYAEPPAKEEDYAEPPAKEEAHSMDSMDSMDSMDSMDSMDSMHSMDSMDSMDSMDSMHSMDSMDSMDSM...DSMHSMDSMDSMDSMDSMDSMDSMHSMDTSIDAVDAAANVTDAADTAGAAANVTDAADTAGAAAEEKPPENASVDSLDSLLDG

  3. Protein (Viridiplantae): 832567 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available NYNSMNASIEIKQQESCQTNINHESCMFSKCMGGMQRFAIPPLPSFEVEQLNVVQGSRHCLSPHFQNSLVTFISYQKEKES... ... 1003877:124 ... 3655:124 ... 3656:1142 ... PREDICTED: transcription factor bHLH143-like Cucumis melo MVGTDTWQLH

  4. Protein (Viridiplantae): 128519 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 9:7 3650:7 ... 1003877:7 ... 3655:7 ... 3656:1095 ... PREDICTED: pectinesterase inhibitor-like Cucumis melo MANNSCLV...IVSLIGVLLFTIILNVASSNYVISTICSKSSNPPFCSSVLKSSGTTYLKGLAVYTLNLAHTNASKSLTLARSLATTTTNPQ

  5. Integration of Fast Predictive Model and SLM Process Development Chamber, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This STTR project seeks to develop a fast predictive model for selective laser melting (SLM) processes and then integrate that model with an SLM chamber that allows...

  6. Integrating prediction, provenance, and optimization into high energy workflows

    Energy Technology Data Exchange (ETDEWEB)

    Schram, M.; Bansal, V.; Friese, R. D.; Tallent, N. R.; Yin, J.; Barker, K. J.; Stephan, E.; Halappanavar, M.; Kerbyson, D. J.

    2017-10-01

    We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.

  7. Improving Permafrost Hydrology Prediction Through Data-Model Integration

    Science.gov (United States)

    Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

    2017-12-01

    The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

  8. Predictions of integrated circuit serviceability in space radiation fields

    Energy Technology Data Exchange (ETDEWEB)

    Khamidullina, N.M.; Kuznetsov, N.V.; Pichkhadze, K.M.; Popov, V.D

    1999-10-01

    The present paper suggests an approach to estimating and predicting the serviceability of on-board electronic equipment. It is based on the postulates of the reliability theory and accounts for total-dose and single-event radiation effects as well as other exterior destabilizing factors. The methods of determination of failure and upset rates for CMOS devices are considered. The probability of non-failure operation of a two CMOS RAM is calculated along the whole trajectory of the 'Solar Probe' spacecraft.

  9. PhosphOrtholog: a web-based tool for cross-species mapping of orthologous protein post-translational modifications.

    Science.gov (United States)

    Chaudhuri, Rima; Sadrieh, Arash; Hoffman, Nolan J; Parker, Benjamin L; Humphrey, Sean J; Stöckli, Jacqueline; Hill, Adam P; James, David E; Yang, Jean Yee Hwa

    2015-08-19

    Most biological processes are influenced by protein post-translational modifications (PTMs). Identifying novel PTM sites in different organisms, including humans and model organisms, has expedited our understanding of key signal transduction mechanisms. However, with increasing availability of deep, quantitative datasets in diverse species, there is a growing need for tools to facilitate cross-species comparison of PTM data. This is particularly important because functionally important modification sites are more likely to be evolutionarily conserved; yet cross-species comparison of PTMs is difficult since they often lie in structurally disordered protein domains. Current tools that address this can only map known PTMs between species based on known orthologous phosphosites, and do not enable the cross-species mapping of newly identified modification sites. Here, we addressed this by developing a web-based software tool, PhosphOrtholog ( www.phosphortholog.com ) that accurately maps protein modification sites between different species. This facilitates the comparison of datasets derived from multiple species, and should be a valuable tool for the proteomics community. Here we describe PhosphOrtholog, a web-based application for mapping known and novel orthologous PTM sites from experimental data obtained from different species. PhosphOrtholog is the only generic and automated tool that enables cross-species comparison of large-scale PTM datasets without relying on existing PTM databases. This is achieved through pairwise sequence alignment of orthologous protein residues. To demonstrate its utility we apply it to two sets of human and rat muscle phosphoproteomes generated following insulin and exercise stimulation, respectively, and one publicly available mouse phosphoproteome following cellular stress revealing high mapping and coverage efficiency. Although coverage statistics are dataset dependent, PhosphOrtholog increased the number of cross-species mapped sites

  10. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    Directory of Open Access Journals (Sweden)

    J. Cho

    2016-10-01

    Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  11. Integrated Simulation for HVAC Performance Prediction: State-of-the-Art Illustration

    NARCIS (Netherlands)

    Hensen, J.L.M.; Clarke, J.A.

    2000-01-01

    This paper aims to outline the current state-of-the-art in integrated building simulation for performance prediction of heating, ventilating and air-conditioning (HVAC) systems. The ESP-r system is used as an example where integrated simulation is a core philosophy behind the development. The

  12. Using Advanced Data Mining And Integration In Environmental Prediction Scenarios

    Directory of Open Access Journals (Sweden)

    Habala Ondrej

    2012-01-01

    Full Text Available We present one of the meteorological and hydrological experiments performed in the FP7 project ADMIRE. It serves as an experimental platform for hydrologists, and we have used it also as a testing platform for a suite of advanced data integration and data mining (DMI tools, developed within ADMIRE. The idea of ADMIRE is to develop an advanced DMI platform accessible even to users who are not familiar with data mining techniques. To this end, we have designed a novel DMI architecture, supported by a set of software tools, managed by DMI process descriptions written in a specialized high-level DMI language called DISPEL, and controlled via several different user interfaces, each performing a different set of tasks and targeting different user group.

  13. Predicted performance of an integrated modular engine system

    Science.gov (United States)

    Binder, Michael; Felder, James L.

    1993-01-01

    Space vehicle propulsion systems are traditionally comprised of a cluster of discrete engines, each with its own set of turbopumps, valves, and a thrust chamber. The Integrated Modular Engine (IME) concept proposes a vehicle propulsion system comprised of multiple turbopumps, valves, and thrust chambers which are all interconnected. The IME concept has potential advantages in fault-tolerance, weight, and operational efficiency compared with the traditional clustered engine configuration. The purpose of this study is to examine the steady-state performance of an IME system with various components removed to simulate fault conditions. An IME configuration for a hydrogen/oxygen expander cycle propulsion system with four sets of turbopumps and eight thrust chambers has been modeled using the Rocket Engine Transient Simulator (ROCETS) program. The nominal steady-state performance is simulated, as well as turbopump thrust chamber and duct failures. The impact of component failures on system performance is discussed in the context of the system's fault tolerant capabilities.

  14. Protein (Viridiplantae): 850873 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available OC100796589 Glycine max MEETSWEQRVQALTHILTSPTTTPSLHSQFFIATQIPCYLNWDYPPFLCSSNPQLLK...803:12853 ... 3814:12853 ... 163735:5410 ... 3846:8024 1462606:8024 3847:8024 ... PREDICTED: uncharacterized protein L

  15. Protein (Viridiplantae): 175805 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 745:703 ... 171637:1864 ... 721813:1696 ... 3749:1696 ... 3750:1696 ... PREDICTED: cytochrome P450 87A3-like Malus domest...ica MWSLVGLSFLVSLVVIFITPWIXKWRYPKCNGALPPGSMGLPFIGETLSLIIPSYSHDLLPFIKKRVRRYGPIFRTS

  16. Protein (Viridiplantae): 175804 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 745:703 ... 171637:1864 ... 721813:1696 ... 3749:1696 ... 3750:1696 ... PREDICTED: cytochrome P450 87A3-like Malus domest...ica MWSLVGLSFLVSLVVIFITPWIXKWRYPKCNGALPPGSMGLPFIGETLSLIIPSYSHDLLPFIKKRVRRYGPIFRTS

  17. Protein (Viridiplantae): 816751 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 0:266 ... 424551:266 ... 424574:266 ... 4107:266 ... 4113:1088 ... PREDICTED: proteoglycan 4-like Solanum tuberosum MPTLSKLEIPNSPNPET...PGSPKSVTPSISKPKTPSFSKPETPSFSTPETPSFSRPETPSFSKPETPSSSKPEAPSSLTPETPSFSKPETLSFSKPET...PSSPKLEIRNSAKPETPSFSKPETPSFSKPKTPSSPKPETPSFSKPKTPSSPNLKTPTPSSPNSQTPSFSNSRKPEAPTFLKPETPSSPKPKTPSFSTPETPTFSKPET...PNFSKSETPSFSKPETPSSFKPETHSFLKSETPSSPKPETPSSPKFEPPSSPKPETPSSPKTENPSSPNTETPNFSKPETPSSPKPNTPSFPKLDTPSFSNPKTPSYETPSFPKFETTSSQKPETPNSPKFGTPSLPKSKIPSDPIFETISFSKPETSNSSKPKIPTTP

  18. Protein (Viridiplantae): 41039 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available s mume MSKSKGRFQLPSSSSVISCMFSLSKQKQEESLDDDTQTQKDEWKKTLIGKLGNVHQREYLSHYGRGRKGKIRGE...403 3745:2403 ... 171637:3833 721805:8 ... 3754:8 ... 102107:3050 ... PREDICTED: UPF0481 protein At3g47200-like Prunu

  19. Protein (Viridiplantae): 94306 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 47368:3137 ... 147385:3137 ... 15367:3137 ... 15368:3137 ... PREDICTED: bifunctional monodehydroascorbate reductase and carbonic anhydrase nect...arin-3-like Brachypodium distachyon MATRVGNAVVFALLLCARFL

  20. Protein (Viridiplantae): 128879 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 5:248 3803:248 ... 3814:248 ... 163722:3689 ... 3826:3689 ... 3827:3689 ... PREDICTED: uncharacterized protein LOC101515541 Cicer arietinum MNSSTI...CSLFLGLILISQSANAKGHGGGLVVTICKGATDRAACENILGSNSEISHAKSFSQL

  1. IONS: Identification of Orthologs by Neighborhood and Similarity—an Automated Method to Identify Orthologs in Chromosomal Regions of Common Evolutionary Ancestry and its Application to Hemiascomycetous Yeasts

    Science.gov (United States)

    Seret, Marie-Line; Baret, Philippe V.

    2011-01-01

    Comparative sequence analysis is widely used to infer gene function and study genome evolution and requires proper ortholog identification across different genomes. We have developed a program for the Identification of Orthologs in one-to-one relationship by Neighborhood and Similarity (IONS) between closely related species. The algorithm combines two levels of evidence to determine co-ancestrality at the genome scale: sequence similarity and shared neighborhood. The method was initially designed to provide anchor points for syntenic blocks within the Génolevures project concerning nine hemiascomycetous yeasts (about 50,000 genes) and is applicable to different input databases. Comparison based on use of a Rand index shows that the results are highly consistent with the pillars of the Yeast Gene Order Browser, a manually curated database. Compared with SYNERGY, another algorithm reporting homology relationships, our method’s main advantages are its automation and the absence of dataset-dependent parameters, facilitating consistent integration of newly released genomes. PMID:21918595

  2. Integrating chemical footprinting data into RNA secondary structure prediction.

    Directory of Open Access Journals (Sweden)

    Kourosh Zarringhalam

    Full Text Available Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension, yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints, which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

  3. Integrating remotely sensed fires for predicting deforestation for REDD.

    Science.gov (United States)

    Armenteras, Dolors; Gibbes, Cerian; Anaya, Jesús A; Dávalos, Liliana M

    2017-06-01

    Fire is an important tool in tropical forest management, as it alters forest composition, structure, and the carbon budget. The United Nations program on Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to sustainably manage forests, as well as to conserve and enhance their carbon stocks. Despite the crucial role of fire management, decision-making on REDD+ interventions fails to systematically include fires. Here, we address this critical knowledge gap in two ways. First, we review REDD+ projects and programs to assess the inclusion of fires in monitoring, reporting, and verification (MRV) systems. Second, we model the relationship between fire and forest for a pilot site in Colombia using near-real-time (NRT) fire monitoring data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The literature review revealed fire remains to be incorporated as a key component of MRV systems. Spatially explicit modeling of land use change showed the probability of deforestation declined sharply with increasing distance to the nearest fire the preceding year (multi-year model area under the curve [AUC] 0.82). Deforestation predictions based on the model performed better than the official REDD early-warning system. The model AUC for 2013 and 2014 was 0.81, compared to 0.52 for the early-warning system in 2013 and 0.68 in 2014. This demonstrates NRT fire monitoring is a powerful tool to predict sites of forest deforestation. Applying new, publicly available, and open-access NRT fire data should be an essential element of early-warning systems to detect and prevent deforestation. Our results provide tools for improving both the current MRV systems, and the deforestation early-warning system in Colombia. © 2017 by the Ecological Society of America.

  4. A multimetric approach for predicting the ecological integrity of New Zealand streams

    Directory of Open Access Journals (Sweden)

    Clapcott J.E.

    2014-01-01

    Full Text Available Integrating multiple measures of stream health into a combined metric can provide a holistic assessment of the ecological integrity of a stream. The aim of this study was to develop a multimetric index (MMI of stream integrity based on predictive modelling of national data sets of water quality, macroinvertebrates, fish and ecosystem process metrics. We used a boosted regression tree approach to calculate an observed/expected score for each metric prior to combining metrics in a MMI based on data availability and the strength of predictive models. The resulting MMI provides a geographically meaningful prediction of the ecological integrity of rivers in New Zealand, but identifies limitations in data and approach, providing focus for ongoing research.

  5. Integrated Solid Oxide Fuel Cell Power System Characteristics Prediction

    Directory of Open Access Journals (Sweden)

    Marian GAICEANU

    2009-07-01

    Full Text Available The main objective of this paper is to deduce the specific characteristics of the CHP 100kWe Solid Oxide Fuel Cell (SOFC Power System from the steady state experimental data. From the experimental data, the authors have been developed and validated the steady state mathematical model. From the control room the steady state experimental data of the SOFC power conditioning are available and using the developed steady state mathematical model, the authors have been obtained the characteristic curves of the system performed by Siemens-Westinghouse Power Corporation. As a methodology the backward and forward power flow analysis has been employed. The backward power flow makes possible to obtain the SOFC power system operating point at different load levels, resulting as the load characteristic. By knowing the fuel cell output characteristic, the forward power flow analysis is used to predict the power system efficiency in different operating points, to choose the adequate control decision in order to obtain the high efficiency operation of the SOFC power system at different load levels. The CHP 100kWe power system is located at Gas Turbine Technologies Company (a Siemens Subsidiary, TurboCare brand in Turin, Italy. The work was carried out through the Energia da Ossidi Solidi (EOS Project. The SOFC stack delivers constant power permanently in order to supply the electric and thermal power both to the TurboCare Company and to the national grid.

  6. Integrated model for predicting rice yield with climate change

    Science.gov (United States)

    Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

    2018-04-01

    Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

  7. Protein (Viridiplantae): 356536840 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 24:2780 3398:2780 71240:414 91827:414 71275:1623 91835:562 72025:981 3803:981 3814:981 163735:1026 3846:1026 3847:1026 PREDICTED: pat...ellin-4-like Glycine max MNNTNECCCNDENGKIVVGVPLVFNFFDKNTNNIEKSLKVQLEKKNQLEDHDCDQEDD

  8. Protein (Viridiplantae): 356543245 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 24:2780 3398:2780 71240:414 91827:414 71275:1623 91835:562 72025:981 3803:981 3814:981 163735:1026 3846:1026 3847:1026 PREDICTED: pat...ellin-3-like Glycine max MAQNDSNPTPPPEPHVAAEPITEDLVQDKEEEDDSSKIVIPVPESESLSLKEDSNRVS

  9. Protein (Viridiplantae): 356506815 [PGDBj - Ortholog DB

    Lifescience Database Archive (English)

    Full Text Available 24:2780 3398:2780 71240:414 91827:414 71275:1623 91835:562 72025:981 3803:981 3814:981 163735:1026 3846:1026 3847:1026 PREDICTED: pat...ellin-6-like Glycine max MMDTTSSPLSLQTQKTTFQELPEASPKPYKKGIVATLMGGGLFKEDNYFVSLLRSSEK

  10. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences

    OpenAIRE

    Huerta-Cepas, J.; Szklarczyk, D.; Forslund, K.; Cook, H.; Heller, D.; Walter, M.C.; Rattei, T.; Mende, D.R.; Sunagawa, S.; Kuhn, M.; Jensen, L.J.; von Mering, C.; Bork, P.

    2016-01-01

    eggNOG is a public resource that provides Orthologous Groups (OGs) of proteins at different taxonomic levels, each with integrated and summarized functional annotations. Developments since the latest public release include changes to the algorithm for creating OGs across taxonomic levels, making nested groups hierarchically consistent. This allows for a better propagation of functional terms across nested OGs and led to the novel annotation of 95 890 previously uncharacterized OGs, increasing...

  11. Ortholog - MicrobeDB.jp | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available List Contact us MicrobeDB.jp Ortholog Data detail Data name Ortholog DOI 10.18908/lsdba.nbdc01181-010.V002 V...814 triples - About This Database Database Description Download License Update History of This Database Site Policy | Contact Us Ortholog - MicrobeDB.jp | LSDB Archive ...

  12. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  13. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Predicting cycle time distributions for integrated processing workstations : an aggregate modeling approach

    NARCIS (Netherlands)

    Veeger, C.P.L.; Etman, L.F.P.; Lefeber, A.A.J.; Adan, I.J.B.F.; Herk, van J.; Rooda, J.E.

    2011-01-01

    To predict cycle time distributions of integrated processing workstations, detailed simulation models are almost exclusively used; these models require considerable development and maintenance effort. As an alternative, we propose an aggregate model that is a lumped-parameter representation of the

  15. Predicting Examination Performance Using an Expanded Integrated Hierarchical Model of Test Emotions and Achievement Goals

    Science.gov (United States)

    Putwain, Dave; Deveney, Carolyn

    2009-01-01

    The aim of this study was to examine an expanded integrative hierarchical model of test emotions and achievement goal orientations in predicting the examination performance of undergraduate students. Achievement goals were theorised as mediating the relationship between test emotions and performance. 120 undergraduate students completed…

  16. COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

    Science.gov (United States)

    Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

    2016-01-01

    Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

  17. Factors Influencing College Women's Contraceptive Behavior: An Application of the Integrative Model of Behavioral Prediction

    Science.gov (United States)

    Sutton, Jazmyne A.; Walsh-Buhi, Eric R.

    2017-01-01

    Objective: This study investigated variables within the Integrative Model of Behavioral Prediction (IMBP) as well as differences across socioeconomic status (SES) levels within the context of inconsistent contraceptive use among college women. Participants: A nonprobability sample of 515 female college students completed an Internet-based survey…

  18. Testing Predictive Models of Technology Integration in Mexico and the United States

    Science.gov (United States)

    Velazquez, Cesareo Morales

    2008-01-01

    Data from Mexico City, Mexico (N = 978) and from Texas, USA (N = 932) were used to test the predictive validity of the teacher professional development component of the Will, Skill, Tool Model of Technology Integration in a cross-cultural context. Structural equation modeling (SEM) was used to test the model. Analyses of these data yielded…

  19. Predicting Elementary Education Candidates' Technology Integration during Their Field Placement Instruction.

    Science.gov (United States)

    Negishi, Meiko; Elder, Anastasia D.; Hamil, J. Burnette; Mzoughi, Taha

    A growing concern in teacher education programs is technology training. Research confirms that training positively affects perservice teachers' attitudes and technology proficiency. However, little is known about the kinds of factors that may predict preservice teachers' integration of technology into their own instruction. The goal of this study…

  20. Structural integrity of frontostriatal connections predicts longitudinal changes in self-esteem.

    Science.gov (United States)

    Chavez, Robert S; Heatherton, Todd F

    2017-06-01

    Diverse neurological and psychiatric conditions are marked by a diminished sense of positive self-regard, and reductions in self-esteem are associated with risk for these disorders. Recent evidence has shown that the connectivity of frontostriatal circuitry reflects individual differences in self-esteem. However, it remains an open question as to whether the integrity of these connections can predict self-esteem changes over larger timescales. Using diffusion magnetic resonance imaging and probabilistic tractography, we demonstrate that the integrity of white matter pathways linking the medial prefrontal cortex to the ventral striatum predicts changes in self-esteem 8 months after initial scanning in a sample of 30 young adults. Individuals with greater integrity of this pathway during the scanning session at Time 1 showed increased levels of self-esteem at follow-up, whereas individuals with lower integrity showed stifled or decreased levels of self-esteem. These results provide evidence that frontostriatal white matter integrity predicts the trajectory of self-esteem development in early adulthood, which may contribute to blunted levels of positive self-regard seen in multiple psychiatric conditions, including depression and anxiety.

  1. Integrative approaches to the prediction of protein functions based on the feature selection

    Directory of Open Access Journals (Sweden)

    Lee Hyunju

    2009-12-01

    Full Text Available Abstract Background Protein function prediction has been one of the most important issues in functional genomics. With the current availability of various genomic data sets, many researchers have attempted to develop integration models that combine all available genomic data for protein function prediction. These efforts have resulted in the improvement of prediction quality and the extension of prediction coverage. However, it has also been observed that integrating more data sources does not always increase the prediction quality. Therefore, selecting data sources that highly contribute to the protein function prediction has become an important issue. Results We present systematic feature selection methods that assess the contribution of genome-wide data sets to predict protein functions and then investigate the relationship between genomic data sources and protein functions. In this study, we use ten different genomic data sources in Mus musculus, including: protein-domains, protein-protein interactions, gene expressions, phenotype ontology, phylogenetic profiles and disease data sources to predict protein functions that are labelled with Gene Ontology (GO terms. We then apply two approaches to feature selection: exhaustive search feature selection using a kernel based logistic regression (KLR, and a kernel based L1-norm regularized logistic regression (KL1LR. In the first approach, we exhaustively measure the contribution of each data set for each function based on its prediction quality. In the second approach, we use the estimated coefficients of features as measures of contribution of data sources. Our results show that the proposed methods improve the prediction quality compared to the full integration of all data sources and other filter-based feature selection methods. We also show that contributing data sources can differ depending on the protein function. Furthermore, we observe that highly contributing data sets can be similar among

  2. Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Jaroslav Solc

    2009-06-01

    The Energy & Environmental Research Center (EERC) completed a brief evaluation of the existing status of predictive modeling to assess options for integration of our previous paleohydrologic reconstructions and their synthesis with current global climate scenarios. Results of our research indicate that short-term data series available from modern instrumental records are not sufficient to reconstruct past hydrologic events or predict future ones. On the contrary, reconstruction of paleoclimate phenomena provided credible information on past climate cycles and confirmed their integration in the context of regional climate history is possible. Similarly to ice cores and other paleo proxies, acquired data represent an objective, credible tool for model calibration and validation of currently observed trends. It remains a subject of future research whether further refinement of our results and synthesis with regional and global climate observations could contribute to improvement and credibility of climate predictions on a regional and global scale.

  3. At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration.

    Science.gov (United States)

    Stegen, James C

    2018-01-01

    To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modeling frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. We can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.

  4. Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method.

    Science.gov (United States)

    Li, Jing; Wang, Jing; Li, Jing

    2017-12-01

    As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREAL W , which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREAL W has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREAL W could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .

  5. Identification, developmental expression and regulation of the Xenopus ortholog of human FANCG/XRCC9.

    Science.gov (United States)

    Stone, Stacie; Sobeck, Alexandra; van Kogelenberg, Margriet; de Graaf, Bendert; Joenje, Hans; Christian, Jan; Hoatlin, Maureen E

    2007-07-01

    Fanconi anemia (FA) is associated with variable developmental abnormalities, bone marrow failure and cancer susceptibility. FANCG/XRCC9 is member of the FA core complex, a group of proteins that control the monoubiquitylation of FANCD2, an event that plays a critical role in maintaining genomic stability. Here we report the identification of the Xenopus laevis ortholog of human FANCG (xFANCG), its expression during development, and its molecular interactions with a partner protein, xFANCA. The xFANCG protein sequence is 47% similar to its human ortholog, with highest conservation in the two putative N-terminal leucine zippers and the tetratricopeptide repeat (TPR) motifs. xFANCG is maternally and zygotically transcribed. Prior to the midblastula stage, a single xFANCG transcript is observed but two additional alternatively spliced mRNAs are detected after the midblastula transition. One of the variants is predicted to encode a novel isoform of xFANCG lacking exon 2. The mutual association between FANCG and FANCA required for their nuclear import is conserved in Xenopus egg extracts. Our data demonstrate that interactions between FANCA and FANCG occur at the earliest stage of vertebrate development and raise the possibility that functionally different isoforms of xFANCG may play a role in early development.

  6. Ortholog-based screening and identification of genes related to intracellular survival.

    Science.gov (United States)

    Yang, Xiaowen; Wang, Jiawei; Bing, Guoxia; Bie, Pengfei; De, Yanyan; Lyu, Yanli; Wu, Qingmin

    2018-04-20

    Bioinformatics and comparative genomics analysis methods were used to predict unknown pathogen genes based on homology with identified or functionally clustered genes. In this study, the genes of common pathogens were analyzed to screen and identify genes associated with intracellular survival through sequence similarity, phylogenetic tree analysis and the λ-Red recombination system test method. The total 38,952 protein-coding genes of common pathogens were divided into 19,775 clusters. As demonstrated through a COG analysis, information storage and processing genes might play an important role intracellular survival. Only 19 clusters were present in facultative intracellular pathogens, and not all were present in extracellular pathogens. Construction of a phylogenetic tree selected 18 of these 19 clusters. Comparisons with the DEG database and previous research revealed that seven other clusters are considered essential gene clusters and that seven other clusters are associated with intracellular survival. Moreover, this study confirmed that clusters screened by orthologs with similar function could be replaced with an approved uvrY gene and its orthologs, and the results revealed that the usg gene is associated with intracellular survival. The study improves the current understanding of intracellular pathogens characteristics and allows further exploration of the intracellular survival-related gene modules in these pathogens. Copyright © 2018. Published by Elsevier B.V.

  7. [Integrity].

    Science.gov (United States)

    Gómez Rodríguez, Rafael Ángel

    2014-01-01

    To say that someone possesses integrity is to claim that that person is almost predictable about responses to specific situations, that he or she can prudentially judge and to act correctly. There is a closed interrelationship between integrity and autonomy, and the autonomy rests on the deeper moral claim of all humans to integrity of the person. Integrity has two senses of significance for medical ethic: one sense refers to the integrity of the person in the bodily, psychosocial and intellectual elements; and in the second sense, the integrity is the virtue. Another facet of integrity of the person is la integrity of values we cherish and espouse. The physician must be a person of integrity if the integrity of the patient is to be safeguarded. The autonomy has reduced the violations in the past, but the character and virtues of the physician are the ultimate safeguard of autonomy of patient. A field very important in medicine is the scientific research. It is the character of the investigator that determines the moral quality of research. The problem arises when legitimate self-interests are replaced by selfish, particularly when human subjects are involved. The final safeguard of moral quality of research is the character and conscience of the investigator. Teaching must be relevant in the scientific field, but the most effective way to teach virtue ethics is through the example of the a respected scientist.

  8. Integrated petrophysical and reservoir characterization workflow to enhance permeability and water saturation prediction

    Science.gov (United States)

    Al-Amri, Meshal; Mahmoud, Mohamed; Elkatatny, Salaheldin; Al-Yousef, Hasan; Al-Ghamdi, Tariq

    2017-07-01

    Accurate estimation of permeability is essential in reservoir characterization and in determining fluid flow in porous media which greatly assists optimize the production of a field. Some of the permeability prediction techniques such as Porosity-Permeability transforms and recently artificial intelligence and neural networks are encouraging but still show moderate to good match to core data. This could be due to limitation to homogenous media while the knowledge about geology and heterogeneity is indirectly related or absent. The use of geological information from core description as in Lithofacies which includes digenetic information show a link to permeability when categorized into rock types exposed to similar depositional environment. The objective of this paper is to develop a robust combined workflow integrating geology and petrophysics and wireline logs in an extremely heterogeneous carbonate reservoir to accurately predict permeability. Permeability prediction is carried out using pattern recognition algorithm called multi-resolution graph-based clustering (MRGC). We will bench mark the prediction results with hard data from core and well test analysis. As a result, we showed how much better improvements are achieved in the permeability prediction when geology is integrated within the analysis. Finally, we use the predicted permeability as an input parameter in J-function and correct for uncertainties in saturation calculation produced by wireline logs using the classical Archie equation. Eventually, high level of confidence in hydrocarbon volumes estimation is reached when robust permeability and saturation height functions are estimated in presence of important geological details that are petrophysically meaningful.

  9. Predicting co-complexed protein pairs using genomic and proteomic data integration

    Directory of Open Access Journals (Sweden)

    King Oliver D

    2004-04-01

    Full Text Available Abstract Background Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H and affinity purification coupled with mass spectrometry (APMS have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. Results Using a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue, a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database, and the remaining predictions may potentially represent unknown CCPs. Conclusions We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

  10. Predictive Coding and Multisensory Integration: An Attentional Account of the Multisensory Mind

    Directory of Open Access Journals (Sweden)

    Durk eTalsma

    2015-03-01

    Full Text Available Multisensory integration involves a host of different cognitive processes, occurring at different stages of sensory processing. Here I argue that, despite recent insights suggesting that multisensory interactions can occur at very early latencies, the actual integration of individual sensory traces into an internally consistent mental representation is dependent on both top-down and bottom-up processes. Moreover, I argue that this integration is not limited to just sensory inputs, but that internal cognitive processes also shape the resulting mental representation. Studies showing that memory recall is affected by the initial multisensory context in which the stimuli were presented will be discussed, as well as several studies showing that mental imagery can affect multisensory illusions. This empirical evidence will be discussed from a predictive coding perspective, in which a central top-down attentional process is proposed to play a central role in coordinating the integration of all these inputs into a coherent mental representation.

  11. Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design.

    Science.gov (United States)

    Spreco, Armin; Eriksson, Olle; Dahlström, Örjan; Cowling, Benjamin John; Timpka, Toomas

    2017-06-15

    Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning

  12. Integrating Models of Diffusion and Behavior to Predict Innovation Adoption, Maintenance, and Social Diffusion.

    Science.gov (United States)

    Smith, Rachel A; Kim, Youllee; Zhu, Xun; Doudou, Dimi Théodore; Sternberg, Eleanore D; Thomas, Matthew B

    2018-01-01

    This study documents an investigation into the adoption and diffusion of eave tubes, a novel mosquito vector control, during a large-scale scientific field trial in West Africa. The diffusion of innovations (DOI) and the integrated model of behavior (IMB) were integrated (i.e., innovation attributes with attitudes and social pressures with norms) to predict participants' (N = 329) diffusion intentions. The findings showed that positive attitudes about the innovation's attributes were a consistent positive predictor of diffusion intentions: adopting it, maintaining it, and talking with others about it. As expected by the DOI and the IMB, the social pressure created by a descriptive norm positively predicted intentions to adopt and maintain the innovation. Drawing upon sharing research, we argued that the descriptive norm may dampen future talk about the innovation, because it may no longer be seen as a novel, useful topic to discuss. As predicted, the results showed that as the descriptive norm increased, the intention to talk about the innovation decreased. These results provide broad support for integrating the DOI and the IMB to predict diffusion and for efforts to draw on other research to understand motivations for social diffusion.

  13. An electrically actuated imperfect microbeam: Dynamical integrity for interpreting and predicting the device response

    KAUST Repository

    Ruzziconi, Laura

    2013-02-20

    In this study we deal with a microelectromechanical system (MEMS) and develop a dynamical integrity analysis to interpret and predict the experimental response. The device consists of a clamped-clamped polysilicon microbeam, which is electrostatically and electrodynamically actuated. It has non-negligible imperfections, which are a typical consequence of the microfabrication process. A single-mode reduced-order model is derived and extensive numerical simulations are performed in a neighborhood of the first symmetric natural frequency, via frequency response diagrams and behavior chart. The typical softening behavior is observed and the overall scenario is explored, when both the frequency and the electrodynamic voltage are varied. We show that simulations based on direct numerical integration of the equation of motion in time yield satisfactory agreement with the experimental data. Nevertheless, these theoretical predictions are not completely fulfilled in some aspects. In particular, the range of existence of each attractor is smaller in practice than in the simulations. This is because these theoretical curves represent the ideal limit case where disturbances are absent, which never occurs under realistic conditions. A reliable prediction of the actual (and not only theoretical) range of existence of each attractor is essential in applications. To overcome this discrepancy and extend the results to the practical case where disturbances exist, a dynamical integrity analysis is developed. After introducing dynamical integrity concepts, integrity profiles and integrity charts are drawn. They are able to describe if each attractor is robust enough to tolerate the disturbances. Moreover, they detect the parameter range where each branch can be reliably observed in practice and where, instead, becomes vulnerable, i.e. they provide valuable information to operate the device in safe conditions according to the desired outcome and depending on the expected disturbances

  14. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

    2012-01-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

  15. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

    2012-07-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

  16. Analysis of predicted and measured performance of an integrated compound parabolic concentrator (ICPC)

    Energy Technology Data Exchange (ETDEWEB)

    Winston, R.; O' Gallagher, J.J.; Muschaweck, J.; Mahoney, A.R.; Dudley, V.

    1999-07-01

    A variety of configurations of evacuated Integrated Compound Parabolic Concentrator (ICPC) tubes have been under development for many years. A particularly favorable optical design corresponds to the unit concentration limit for a fin CPC solution which is then coupled to a practical, thin, wedge-shaped absorber. Prototype collector modules using tubes with two different fin orientations (horizontal and vertical) have been fabricated and tested. Comprehensive measurements of the optical characteristics of the reflector and absorber have been used together with a detailed ray trace analysis to predict the optical performance characteristics of these designs. The observed performance agrees well with the predicted performance.

  17. Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

    Directory of Open Access Journals (Sweden)

    Reza Rawassizadeh

    2015-09-01

    Full Text Available As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

  18. Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches.

    Science.gov (United States)

    Rawassizadeh, Reza; Tomitsch, Martin; Nourizadeh, Manouchehr; Momeni, Elaheh; Peery, Aaron; Ulanova, Liudmila; Pazzani, Michael

    2015-09-08

    As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

  19. An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

    Science.gov (United States)

    Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

    2015-11-01

    This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. The Prediction-Focused Approach: An opportunity for hydrogeophysical data integration and interpretation

    Science.gov (United States)

    Hermans, Thomas; Nguyen, Frédéric; Klepikova, Maria; Dassargues, Alain; Caers, Jef

    2017-04-01

    Hydrogeophysics is an interdisciplinary field of sciences aiming at a better understanding of subsurface hydrological processes. If geophysical surveys have been successfully used to qualitatively characterize the subsurface, two important challenges remain for a better quantification of hydrological processes: (1) the inversion of geophysical data and (2) their integration in hydrological subsurface models. The classical inversion approach using regularization suffers from spatially and temporally varying resolution and yields geologically unrealistic solutions without uncertainty quantification, making their utilization for hydrogeological calibration less consistent. More advanced techniques such as coupled inversion allow for a direct use of geophysical data for conditioning groundwater and solute transport model calibration. However, the technique is difficult to apply in complex cases and remains computationally demanding to estimate uncertainty. In a recent study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties from geophysical data, circumventing the need for classic inversions. In PFA, we seek a direct relationship between the data and the subsurface variables we want to predict (the forecast). This relationship is obtained through a prior set of subsurface models for which both data and forecast are computed. A direct relationship can often be derived through dimension reduction techniques. PFA offers a framework for both hydrogeophysical "inversion" and hydrogeophysical data integration. For hydrogeophysical "inversion", the considered forecast variable is the subsurface variable, such as the salinity. An ensemble of possible solutions is generated, allowing uncertainty quantification. For hydrogeophysical data integration, the forecast variable becomes the prediction we want to make with our subsurface models, such as the concentration of contaminant in a drinking water production well. Geophysical

  1. Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

    Directory of Open Access Journals (Sweden)

    Simon-Shlomo ePoil

    2013-10-01

    Full Text Available Alzheimer's disease (AD is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a two-year period. We followed 86 patients initially diagnosed with MCI for two years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/. We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

  2. PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.

    Directory of Open Access Journals (Sweden)

    Jiangning Song

    Full Text Available The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s. Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate

  3. Integrating environmental and genetic effects to predict responses of tree populations to climate.

    Science.gov (United States)

    Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

    2010-01-01

    Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

  4. Brain systems for probabilistic and dynamic prediction: computational specificity and integration.

    Directory of Open Access Journals (Sweden)

    Jill X O'Reilly

    2013-09-01

    Full Text Available A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI we showed that activity in two brain systems (typically associated with reward learning and motor control could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.

  5. Online Sentence Comprehension in PPA: Verb-Based Integration and Prediction

    Directory of Open Access Journals (Sweden)

    Jennifer E Mack

    2015-05-01

    Full Text Available Introduction. Impaired language comprehension is frequently observed in primary progressive aphasia (PPA. Word comprehension deficits are characteristic of the semantic variant (PPA-S whereas sentence comprehension deficits are more prevalent in the agrammatic (PPA-G and logopenic (PPA-L variants (Amici et al., 2007; Gorno-Tempini et al., 2011; Thompson et al., 2013. Word and sentence comprehension deficits have also been shown to have distinct neural substrates in PPA (Mesulam, Thompson, Weintraub, & Rogalski, in press. However, little is known about the relationship between word and sentence comprehension processes in PPA, specifically how words are accessed, combined, and used to predict upcoming elements within a sentence. A previous study demonstrated that listeners with stroke-induced agrammatic aphasia rapidly access verb meanings and use them to semantically integrate verb-arguments; however, they show deficits in using verb meanings predictively (Mack, Ji, & Thompson, 2013. The present study tested whether listeners with PPA are able to access verb meanings and to use this information to integrate and predict verb-arguments. Methods. Fifteen adults with PPA (8 with PPA-G, 3 with PPA-L, and 4 with PPA-S and ten age-matched controls participated in two eyetracking experiments. In both experiments, participants heard sentences with restrictive verbs that were semantically compatible with only one object in a four-picture visual array (e.g., eat when the array included a cake and three non-edible objects and unrestrictive verbs (e.g., move that were compatible with all four objects. The verb-based integration experiment tested access to verb meaning and its effects on integration of the direct object (e.g., Susan will eat/move the cake; the verb-based prediction experiment examined prediction of the direct object (e.g., Susan will eat/move the …. The dependent variable was the rate of fixations on the target picture (e.g., the cake in the

  6. Understanding Eating Behaviors through Parental Communication and the Integrative Model of Behavioral Prediction.

    Science.gov (United States)

    Scheinfeld, Emily; Shim, Minsun

    2017-05-01

    Emerging adulthood (EA) is an important yet overlooked period for developing long-term health behaviors. During these years, emerging adults adopt health behaviors that persist throughout life. This study applies the Integrative Model of Behavioral Prediction (IMBP) to examine the role of childhood parental communication in predicting engagement in healthful eating during EA. Participants included 239 college students, ages 18 to 25, from a large university in the southern United States. Participants were recruited and data collection occurred spring 2012. Participants responded to measures to assess perceived parental communication, eating behaviors, attitudes, subjective norms, and behavioral control over healthful eating. SEM and mediation analyses were used to address the hypotheses posited. Data demonstrated that perceived parent-child communication - specifically, its quality and target-specific content - significantly predicted emerging adults' eating behaviors, mediated through subjective norm and perceived behavioral control. This study sets the stage for further exploration and understanding of different ways parental communication influences emerging adults' healthy behavior enactment.

  7. Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

    Science.gov (United States)

    Li, Longhai; Feng, Cindy X; Qiu, Shi

    2017-06-30

    An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  8. Cluster (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available 0”. This cluster ID is uniquely-assigned by the PGDBj Ortholog Database. Cluster size Number of proteins aff...r About This Database Database Description Download License Update History of This Database Site Policy | Contact Us Cluster (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Cluster (Viridiplantae) Data detail Data name Cluster (Viridiplantae) DO...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  9. Cluster (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available 3090”. This cluster ID is uniquely-assigned by the PGDBj Ortholog Database. Cluster size Number of proteins ...ster About This Database Database Description Download License Update History of This Database Site Policy | Contact Us Cluster (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Cluster (Cyanobacteria) Data detail Data name Cluster (Cyanobacteria) DO...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  10. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Pingzhao Hu

    2006-01-01

    biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. Conclusions: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.

  11. ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

    Science.gov (United States)

    Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

    2013-01-01

    Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application. PMID:23955435

  12. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  13. ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

    Directory of Open Access Journals (Sweden)

    Milos Bogdanovic

    2013-08-01

    Full Text Available Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

  14. ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

    Science.gov (United States)

    Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

    2013-08-15

    Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

  15. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  16. Fast integration-based prediction bands for ordinary differential equation models.

    Science.gov (United States)

    Hass, Helge; Kreutz, Clemens; Timmer, Jens; Kaschek, Daniel

    2016-04-15

    To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. In this article, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org helge.hass@fdm.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

  17. Predicting Athletes’ Pre-Exercise Fluid Intake: A Theoretical Integration Approach

    Directory of Open Access Journals (Sweden)

    Chunxiao Li

    2018-05-01

    Full Text Available Pre-exercise fluid intake is an important healthy behavior for maintaining athletes’ sports performances and health. However, athletes’ behavioral adherence to fluid intake and its underlying psychological mechanisms have not been investigated. This prospective study aimed to use a health psychology model that integrates the self-determination theory and the theory of planned behavior for understanding pre-exercise fluid intake among athletes. Participants (n = 179 were athletes from college sport teams who completed surveys at two time points. Baseline (Time 1 assessment comprised psychological variables of the integrated model (i.e., autonomous and controlled motivation, attitude, subjective norm, perceived behavioral control, and intention and fluid intake (i.e., behavior was measured prospectively at one month (Time 2. Path analysis showed that the positive association between autonomous motivation and intention was mediated by subjective norm and perceived behavioral control. Controlled motivation positively predicted the subjective norm. Intentions positively predicted pre-exercise fluid intake behavior. Overall, the pattern of results was generally consistent with the integrated model, and it was suggested that athletes’ pre-exercise fluid intake behaviors were associated with the motivational and social cognitive factors of the model. The research findings could be informative for coaches and sport scientists to promote athletes’ pre-exercise fluid intake behaviors.

  18. Stock return predictability and market integration: The role of global and local information

    Directory of Open Access Journals (Sweden)

    David G. McMillan

    2016-12-01

    Full Text Available This paper examines the predictability of a range of international stock markets where we allow the presence of both local and global predictive factors. Recent research has argued that US returns have predictive power for international stock returns. We expand this line of research, following work on market integration, to include a more general definition of the global factor, based on principal components analysis. Results identify three global expected returns factors, one related to the major stock markets of the US, UK and Asia and one related to the other markets analysed. The third component is related to dividend growth. A single dominant realised returns factor is also noted. A forecasting exercise comparing the principal components based factors to a US return factor and local market only factors, as well as the historical mean benchmark finds supportive evidence for the former approach. It is hoped that the results from this paper will be informative on three counts. First, to academics interested in understanding the dynamics asset price movement. Second, to market participants who aim to time the market and engage in portfolio and risk management. Third, to those (policy makers and others who are interested in linkages across international markets and the nature and degree of integration.

  19. Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients.

    Science.gov (United States)

    Isik, Zerrin; Ercan, Muserref Ece

    2017-10-01

    Integration of several types of patient data in a computational framework can accelerate the identification of more reliable biomarkers, especially for prognostic purposes. This study aims to identify biomarkers that can successfully predict the potential survival time of a cancer patient by integrating the transcriptomic (RNA-Seq), proteomic (RPPA), and protein-protein interaction (PPI) data. The proposed method -RPBioNet- employs a random walk-based algorithm that works on a PPI network to identify a limited number of protein biomarkers. Later, the method uses gene expression measurements of the selected biomarkers to train a classifier for the survival time prediction of patients. RPBioNet was applied to classify kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LUSC) patients based on their survival time classes (long- or short-term). The RPBioNet method correctly identified the survival time classes of patients with between 66% and 78% average accuracy for three data sets. RPBioNet operates with only 20 to 50 biomarkers and can achieve on average 6% higher accuracy compared to the closest alternative method, which uses only RNA-Seq data in the biomarker selection. Further analysis of the most predictive biomarkers highlighted genes that are common for both cancer types, as they may be driver proteins responsible for cancer progression. The novelty of this study is the integration of a PPI network with mRNA and protein expression data to identify more accurate prognostic biomarkers that can be used for clinical purposes in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Integration of Tuyere, Raceway and Shaft Models for Predicting Blast Furnace Process

    Science.gov (United States)

    Fu, Dong; Tang, Guangwu; Zhao, Yongfu; D'Alessio, John; Zhou, Chenn Q.

    2018-06-01

    A novel modeling strategy is presented for simulating the blast furnace iron making process. Such physical and chemical phenomena are taking place across a wide range of length and time scales, and three models are developed to simulate different regions of the blast furnace, i.e., the tuyere model, the raceway model and the shaft model. This paper focuses on the integration of the three models to predict the entire blast furnace process. Mapping output and input between models and an iterative scheme are developed to establish communications between models. The effects of tuyere operation and burden distribution on blast furnace fuel efficiency are investigated numerically. The integration of different models provides a way to realistically simulate the blast furnace by improving the modeling resolution on local phenomena and minimizing the model assumptions.

  1. Predicting 2D target velocity cannot help 2D motion integration for smooth pursuit initiation.

    Science.gov (United States)

    Montagnini, Anna; Spering, Miriam; Masson, Guillaume S

    2006-12-01

    Smooth pursuit eye movements reflect the temporal dynamics of bidimensional (2D) visual motion integration. When tracking a single, tilted line, initial pursuit direction is biased toward unidimensional (1D) edge motion signals, which are orthogonal to the line orientation. Over 200 ms, tracking direction is slowly corrected to finally match the 2D object motion during steady-state pursuit. We now show that repetition of line orientation and/or motion direction does not eliminate the transient tracking direction error nor change the time course of pursuit correction. Nonetheless, multiple successive presentations of a single orientation/direction condition elicit robust anticipatory pursuit eye movements that always go in the 2D object motion direction not the 1D edge motion direction. These results demonstrate that predictive signals about target motion cannot be used for an efficient integration of ambiguous velocity signals at pursuit initiation.

  2. A Distributed Model Predictive Control approach for the integration of flexible loads, storage and renewables

    DEFF Research Database (Denmark)

    Ferrarini, Luca; Mantovani, Giancarlo; Costanzo, Giuseppe Tommaso

    2014-01-01

    This paper presents an innovative solution based on distributed model predictive controllers to integrate the control and management of energy consumption, energy storage, PV and wind generation at customer side. The overall goal is to enable an advanced prosumer to autoproduce part of the energy...... he needs with renewable sources and, at the same time, to optimally exploit the thermal and electrical storages, to trade off its comfort requirements with different pricing schemes (including real-time pricing), and apply optimal control techniques rather than sub-optimal heuristics....

  3. Model Predictive Control of Grid Connected Modular Multilevel Converter for Integration of Photovoltaic Power Systems

    DEFF Research Database (Denmark)

    Hajizadeh, Amin; Shahirinia, Amir

    2017-01-01

    Investigation of an advanced control structure for integration of Photovoltaic Power Systems through Grid Connected-Modular Multilevel Converter (GC-MMC) is proposed in this paper. To achieve this goal, a non-linear model of MMC regarding considering of negative and positive sequence components has...... been presented. Then, due to existence of unbalance voltage faults in distribution grid, non-linarites and uncertainties in model, model predictive controller which is developed for GC-MMC. They are implemented based upon positive and negative components of voltage and current to mitigate the power...

  4. An integrated numerical model for the prediction of Gaussian and billet shapes

    DEFF Research Database (Denmark)

    Hattel, Jesper; Pryds, Nini; Pedersen, Trine Bjerre

    2004-01-01

    Separate models for the atomisation and the deposition stages were recently integrated by the authors to form a unified model describing the entire spray-forming process. In the present paper, the focus is on describing the shape of the deposited material during the spray-forming process, obtained...... by this model. After a short review of the models and their coupling, the important factors which influence the resulting shape, i.e. Gaussian or billet, are addressed. The key parameters, which are utilized to predict the geometry and dimension of the deposited material, are the sticking efficiency...

  5. Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

    Science.gov (United States)

    Chen, Tianle; Zeng, Donglin

    2015-01-01

    Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

  6. Predicting sugar consumption: Application of an integrated dual-process, dual-phase model.

    Science.gov (United States)

    Hagger, Martin S; Trost, Nadine; Keech, Jacob J; Chan, Derwin K C; Hamilton, Kyra

    2017-09-01

    Excess consumption of added dietary sugars is related to multiple metabolic problems and adverse health conditions. Identifying the modifiable social cognitive and motivational constructs that predict sugar consumption is important to inform behavioral interventions aimed at reducing sugar intake. We tested the efficacy of an integrated dual-process, dual-phase model derived from multiple theories to predict sugar consumption. Using a prospective design, university students (N = 90) completed initial measures of the reflective (autonomous and controlled motivation, intentions, attitudes, subjective norm, perceived behavioral control), impulsive (implicit attitudes), volitional (action and coping planning), and behavioral (past sugar consumption) components of the proposed model. Self-reported sugar consumption was measured two weeks later. A structural equation model revealed that intentions, implicit attitudes, and, indirectly, autonomous motivation to reduce sugar consumption had small, significant effects on sugar consumption. Attitudes, subjective norm, and, indirectly, autonomous motivation to reduce sugar consumption predicted intentions. There were no effects of the planning constructs. Model effects were independent of the effects of past sugar consumption. The model identified the relative contribution of reflective and impulsive components in predicting sugar consumption. Given the prominent role of the impulsive component, interventions that assist individuals in managing cues-to-action and behavioral monitoring are likely to be effective in regulating sugar consumption. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. An Integrated Model to Predict Corporate Failure of Listed Companies in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Nisansala Wijekoon

    2015-07-01

    Full Text Available The primary objective of this study is to develop an integrated model to predict corporate failure of listed companies in Sri Lanka. The logistic regression analysis was employed to a data set of 70 matched-pairs of failed and non-failed companies listed in the Colombo Stock Exchange (CSE in Sri Lanka over the period 2002 to 2010. A total of fifteen financial ratios and eight corporate governance variables were used as predictor variables of corporate failure. Analysis of the statistical testing results indicated that model consists with both corporate governance variables and financial ratios improved the prediction accuracy to reach 88.57 per cent one year prior to failure. Furthermore, predictive accuracy of this model in all three years prior to failure is above 80 per cent. Hence model is robust in obtaining accurate results for up to three years prior to failure. It was further found that two financial ratios, working capital to total assets and cash flow from operating activities to total assets, and two corporate governance variables, outside director ratio and company audit committee are having more explanatory power to predict corporate failure. Therefore, model developed in this study can assist investors, managers, shareholders, financial institutions, auditors and regulatory agents in Sri Lanka to forecast corporate failure of listed companies.

  8. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    Science.gov (United States)

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  9. Taxon (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available of This Database Site Policy | Contact Us Taxon (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Taxon (Viridiplantae) Data detail Data name Taxon (Viridiplantae) DOI 10...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  10. Download - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available e Description Download License Update History of This Database Site Policy | Contact Us Download - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Download First of all, please read the license of this database. Data na...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  11. Protein (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available ut This Database Database Description Download License Update History of This Database Site Policy | Contact Us Protein (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Protein (Cyanobacteria) Data detail Data name Protein (Cyanobacteria) DO...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  12. Taxon (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available of This Database Site Policy | Contact Us Taxon (Cyanobacteria) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Taxon (Cyanobacteria) Data detail Data name Taxon (Cyanobacteria) DOI 10...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  13. Protein (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available ase Description Download License Update History of This Database Site Policy | Contact Us Protein (Viridiplantae) - PGDBj - Ortholog DB | LSDB Archive ... ...List Contact us PGDBj - Ortholog DB Protein (Viridiplantae) Data detail Data name Protein (Viridiplantae) DO...switchLanguage; BLAST Search Image Search Home About Archive Update History Data

  14. Clusters of orthologous genes for 41 archaeal genomes and implications for evolutionary genomics of archaea

    OpenAIRE

    Wolf Yuri I; Novichkov Pavel S; Sorokin Alexander V; Makarova Kira S; Koonin Eugene V

    2007-01-01

    Abstract Background An evolutionary classification of genes from sequenced genomes that distinguishes between orthologs and paralogs is indispensable for genome annotation and evolutionary reconstruction. Shortly after multiple genome sequences of bacteria, archaea, and unicellular eukaryotes became available, an attempt on such a classification was implemented in Clusters of Orthologous Groups of proteins (COGs). Rapid accumulation of genome sequences creates opportunities for refining COGs ...

  15. Integrated prediction based on GIS for sandstone-type uranium deposits in the northwest of Ordos Basin

    International Nuclear Information System (INIS)

    Han Shaoyang; Ke Dan; Hu Shuiqing; Guo Qingyin; Hou Huiqun

    2005-01-01

    The integrated prediction model of sandstone-type uranium deposits and its integrated evaluation methods as well as flow of the work based on GIS are studied. A software for extracting metallogenic information is also developed. A multi-source exploring information database is established in the northwest of Ordos Basin, and an integrated digital mineral deposit prospecting model of sandstone-type uranium deposits is designed based on GIS. The authors have completed metallogenic information extraction and integrated evaluation of sandstone-type uranium deposits based on GIS in the study area. Research results prove that the integrated prediction of sandstone-type uranium deposits based on GIS may further delineate prospective target areas rapidly and improve the predictive precision. (authors)

  16. Thought insertion as a self-disturbance: An integration of predictive coding and phenomenological approaches

    Directory of Open Access Journals (Sweden)

    Philipp Sterzer

    2016-10-01

    Full Text Available Current theories in the framework of hierarchical predictive coding propose that positive symptoms of schizophrenia, such as delusions and hallucinations, arise from an alteration in Bayesian inference, the term inference referring to a process by which learned predictions are used to infer probable causes of sensory data. However, for one particularly striking and frequent symptom of schizophrenia, thought insertion, no plausible account has been proposed in terms of the predictive-coding framework. Here we propose that thought insertion is due to an altered experience of thoughts as coming from nowhere, as is already indicated by the early 20th century phenomenological accounts by the early Heidelberg School of psychiatry. These accounts identified thought insertion as one of the self-disturbances (from German: Ichstörungen of schizophrenia and used mescaline as a model-psychosis in healthy individuals to explore the possible mechanisms. The early Heidelberg School (Gruhle, Mayer-Gross, Beringer first named and defined the self-disturbances, and proposed that thought insertion involves a disruption of the inner connectedness of thoughts and experiences, and a becoming sensory of those thoughts experienced as inserted. This account offers a novel way to integrate the phenomenology of thought insertion with the predictive coding framework. We argue that the altered experience of thoughts may be caused by a reduced precision of context-dependent predictions, relative to sensory precision. According to the principles of Bayesian inference, this reduced precision leads to increased prediction-error signals evoked by the neural activity that encodes thoughts. Thus, in analogy with the prediction-error related aberrant salience of external events that has been proposed previously, internal events such as thoughts (including volitions, emotions and memories can also be associated with increased prediction-error signaling and are thus imbued with

  17. Integrating sequence stratigraphy and rock-physics to interpret seismic amplitudes and predict reservoir quality

    Science.gov (United States)

    Dutta, Tanima

    This dissertation focuses on the link between seismic amplitudes and reservoir properties. Prediction of reservoir properties, such as sorting, sand/shale ratio, and cement-volume from seismic amplitudes improves by integrating knowledge from multiple disciplines. The key contribution of this dissertation is to improve the prediction of reservoir properties by integrating sequence stratigraphy and rock physics. Sequence stratigraphy has been successfully used for qualitative interpretation of seismic amplitudes to predict reservoir properties. Rock physics modeling allows quantitative interpretation of seismic amplitudes. However, often there is uncertainty about selecting geologically appropriate rock physics model and its input parameters, away from the wells. In the present dissertation, we exploit the predictive power of sequence stratigraphy to extract the spatial trends of sedimentological parameters that control seismic amplitudes. These spatial trends of sedimentological parameters can serve as valuable constraints in rock physics modeling, especially away from the wells. Consequently, rock physics modeling, integrated with the trends from sequence stratigraphy, become useful for interpreting observed seismic amplitudes away from the wells in terms of underlying sedimentological parameters. We illustrate this methodology using a comprehensive dataset from channelized turbidite systems, deposited in minibasin settings in the offshore Equatorial Guinea, West Africa. First, we present a practical recipe for using closed-form expressions of effective medium models to predict seismic velocities in unconsolidated sandstones. We use an effective medium model that combines perfectly rough and smooth grains (the extended Walton model), and use that model to derive coordination number, porosity, and pressure relations for P and S wave velocities from experimental data. Our recipe provides reasonable fits to other experimental and borehole data, and specifically

  18. Global identification predicts gay-male identity integration and well-being among Turkish gay men.

    Science.gov (United States)

    Koc, Yasin; Vignoles, Vivian L

    2016-12-01

    In most parts of the world, hegemonic masculinity requires men to endorse traditional masculine ideals, one of which is rejection of homosexuality. Wherever hegemonic masculinity favours heterosexuality over homosexuality, gay males may feel under pressure to negotiate their conflicting male gender and gay sexual identities to maintain positive self-perceptions. However, globalization, as a source of intercultural interaction, might provide a beneficial context for people wishing to create alternative masculinities in the face of hegemonic masculinity. Hence, we tested if global identification would predict higher levels of gay-male identity integration, and indirectly subjective well-being, via alternative masculinity representations for gay and male identities. A community sample of 219 gay and bisexual men from Turkey completed the study. Structural equation modelling revealed that global identification positively predicted gay-male identity integration, and indirectly subjective well-being; however, alternative masculinity representations did not mediate this relationship. Our findings illustrate how identity categories in different domains can intersect and affect each other in complex ways. Moreover, we discuss mental health and well-being implications for gay men living in cultures where they experience high levels of prejudice and stigma. © 2016 The British Psychological Society.

  19. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jin Won [Graduate School of Catholic University of Pusan, Busan (Korea, Republic of); Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo [Dept. Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan (Korea, Republic of)

    2016-12-15

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening.

  20. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

  1. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    International Nuclear Information System (INIS)

    Lee, Jin Won; Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo

    2016-01-01

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening

  2. An integrated Modelling framework to monitor and predict trends of agricultural management (iMSoil)

    Science.gov (United States)

    Keller, Armin; Della Peruta, Raneiro; Schaepman, Michael; Gomez, Marta; Mann, Stefan; Schulin, Rainer

    2014-05-01

    Agricultural systems lay at the interface between natural ecosystems and the anthroposphere. Various drivers induce pressures on the agricultural systems, leading to changes in farming practice. The limitation of available land and the socio-economic drivers are likely to result in further intensification of agricultural land management, with implications on fertilization practices, soil and pest management, as well as crop and livestock production. In order to steer the development into desired directions, tools are required by which the effects of these pressures on agricultural management and resulting impacts on soil functioning can be detected as early as possible, future scenarios predicted and suitable management options and policies defined. In this context, the use of integrated models can play a major role in providing long-term predictions of soil quality and assessing the sustainability of agricultural soil management. Significant progress has been made in this field over the last decades. Some of these integrated modelling frameworks include biophysical parameters, but often the inherent characteristics and detailed processes of the soil system have been very simplified. The development of such tools has been hampered in the past by a lack of spatially explicit soil and land management information at regional scale. The iMSoil project, funded by the Swiss National Science Foundation in the national research programme NRP68 "soil as a resource" (www.nrp68.ch) aims at developing and implementing an integrated modeling framework (IMF) which can overcome the limitations mentioned above, by combining socio-economic, agricultural land management, and biophysical models, in order to predict the long-term impacts of different socio-economic scenarios on the soil quality. In our presentation we briefly outline the approach that is based on an interdisciplinary modular framework that builds on already existing monitoring tools and model components that are

  3. A vision for an ultra-high resolution integrated water cycle observation and prediction system

    Science.gov (United States)

    Houser, P. R.

    2013-05-01

    Society's welfare, progress, and sustainable economic growth—and life itself—depend on the abundance and vigorous cycling and replenishing of water throughout the global environment. The water cycle operates on a continuum of time and space scales and exchanges large amounts of energy as water undergoes phase changes and is moved from one part of the Earth system to another. We must move toward an integrated observation and prediction paradigm that addresses broad local-to-global science and application issues by realizing synergies associated with multiple, coordinated observations and prediction systems. A central challenge of a future water and energy cycle observation strategy is to progress from single variable water-cycle instruments to multivariable integrated instruments in electromagnetic-band families. The microwave range in the electromagnetic spectrum is ideally suited for sensing the state and abundance of water because of water's dielectric properties. Eventually, a dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel active and passive microwave measurements. A partial demonstration of these ideas can even be realized with existing microwave satellite observations to support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information. The simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievals that are not possible with isolated measurements. Furthermore, the simultaneous monitoring of several of the land, atmospheric, oceanic, and cryospheric states brings synergies that will substantially enhance understanding of the global water and energy cycle as a system. The multichannel approach also affords advantages to some constituent retrievals—for instance, simultaneous retrieval of vegetation

  4. Accurate diffraction data integration by the EVAL15 profile prediction method : Application in chemical and biological crystallography

    NARCIS (Netherlands)

    Xian, X.

    2009-01-01

    Accurate integration of reflection intensities plays an essential role in structure determination of the crystallized compound. A new diffraction data integration method, EVAL15, is presented in this thesis. This method uses the principle of general impacts to predict ab inito three-dimensional

  5. Observing others stay or switch - How social prediction errors are integrated into reward reversal learning.

    Science.gov (United States)

    Ihssen, Niklas; Mussweiler, Thomas; Linden, David E J

    2016-08-01

    Reward properties of stimuli can undergo sudden changes, and the detection of these 'reversals' is often made difficult by the probabilistic nature of rewards/punishments. Here we tested whether and how humans use social information (someone else's choices) to overcome uncertainty during reversal learning. We show a substantial social influence during reversal learning, which was modulated by the type of observed behavior. Participants frequently followed observed conservative choices (no switches after punishment) made by the (fictitious) other player but ignored impulsive choices (switches), even though the experiment was set up so that both types of response behavior would be similarly beneficial/detrimental (Study 1). Computational modeling showed that participants integrated the observed choices as a 'social prediction error' instead of ignoring or blindly following the other player. Modeling also confirmed higher learning rates for 'conservative' versus 'impulsive' social prediction errors. Importantly, this 'conservative bias' was boosted by interpersonal similarity, which in conjunction with the lack of effects observed in a non-social control experiment (Study 2) confirmed its social nature. A third study suggested that relative weighting of observed impulsive responses increased with increased volatility (frequency of reversals). Finally, simulations showed that in the present paradigm integrating social and reward information was not necessarily more adaptive to maximize earnings than learning from reward alone. Moreover, integrating social information increased accuracy only when conservative and impulsive choices were weighted similarly during learning. These findings suggest that to guide decisions in choice contexts that involve reward reversals humans utilize social cues conforming with their preconceptions more strongly than cues conflicting with them, especially when the other is similar. Copyright © 2016 The Authors. Published by Elsevier B

  6. An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes.

    Science.gov (United States)

    Shapira, Stav; Novack, Lena; Bar-Dayan, Yaron; Aharonson-Daniel, Limor

    2016-01-01

    A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities' preparedness and response capabilities and to mitigate future consequences. An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model's algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel. the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard. The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

  7. iSPUW: integrated sensing and prediction of urban water for sustainable cities

    Science.gov (United States)

    Noh, S. J.; Nazari, B.; Habibi, H.; Norouzi, A.; Nabatian, M.; Seo, D. J.; Bartos, M. D.; Kerkez, B.; Lakshman, L.; Zink, M.; Lee, J.

    2016-12-01

    Many cities face tremendous water-related challenges in this Century of the City. Urban areas are particularly susceptible not only to excesses and shortages of water but also to impaired water quality. To addresses these challenges, we synergistically integrate advances in computing and cyber-infrastructure, environmental modeling, geoscience, and information science to develop integrative solutions for urban water challenges. In this presentation, we describe the various efforts that are currently ongoing in the Dallas-Fort Worth Metroplex (DFW) area for iSPUW: real-time high-resolution flash flood forecasting, inundation mapping for large urban areas, crowdsourcing of water observations in urban areas, real-time assimilation of crowdsourced observations for street and river flooding, integrated control of lawn irrigation and rainwater harvesting for water conservation and stormwater management, feature mining with causal discovery for flood prediction, and development of the Arlington Urban Hydroinformatics Testbed. Analyzed is the initial data of sensor network for water level and lawn monitoring, and cellphone applications for crowdsourcing flood reports. New data assimilation approaches to deal with categorical and continuous observations are also evaluated via synthetic experiments.

  8. Integration of research infrastructures and ecosystem models toward development of predictive ecology

    Science.gov (United States)

    Luo, Y.; Huang, Y.; Jiang, J.; MA, S.; Saruta, V.; Liang, G.; Hanson, P. J.; Ricciuto, D. M.; Milcu, A.; Roy, J.

    2017-12-01

    The past two decades have witnessed rapid development in sensor technology. Built upon the sensor development, large research infrastructure facilities, such as National Ecological Observatory Network (NEON) and FLUXNET, have been established. Through networking different kinds of sensors and other data collections at many locations all over the world, those facilities generate large volumes of ecological data every day. The big data from those facilities offer an unprecedented opportunity for advancing our understanding of ecological processes, educating teachers and students, supporting decision-making, and testing ecological theory. The big data from the major research infrastructure facilities also provides foundation for developing predictive ecology. Indeed, the capability to predict future changes in our living environment and natural resources is critical to decision making in a world where the past is no longer a clear guide to the future. We are living in a period marked by rapid climate change, profound alteration of biogeochemical cycles, unsustainable depletion of natural resources, and deterioration of air and water quality. Projecting changes in future ecosystem services to the society becomes essential not only for science but also for policy making. We will use this panel format to outline major opportunities and challenges in integrating research infrastructure and ecosystem models toward developing predictive ecology. Meanwhile, we will also show results from an interactive model-experiment System - Ecological Platform for Assimilating Data into models (EcoPAD) - that have been implemented at the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in Northern Minnesota and Montpellier Ecotron, France. EcoPAD is developed by integrating web technology, eco-informatics, data assimilation techniques, and ecosystem modeling. EcoPAD is designed to streamline data transfer seamlessly from research infrastructure

  9. Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation

    KAUST Repository

    Hartono, A. D.; Hakiki, Farizal; Syihab, Z.; Ambia, F.; Yasutra, A.; Sutopo, S.; Efendi, M.; Sitompul, V.; Primasari, I.; Apriandi, R.

    2017-01-01

    EOR preliminary analysis is pivotal to be performed at early stage of assessment in order to elucidate EOR feasibility. This study proposes an in-depth analysis toolkit for EOR preliminary evaluation. The toolkit incorporates EOR screening, predictive, economic, risk analysis and optimisation modules. The screening module introduces algorithms which assimilates statistical and engineering notions into consideration. The United States Department of Energy (U.S. DOE) predictive models were implemented in the predictive module. The economic module is available to assess project attractiveness, while Monte Carlo Simulation is applied to quantify risk and uncertainty of the evaluated project. Optimization scenario of EOR practice can be evaluated using the optimisation module, in which stochastic methods of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES) were applied in the algorithms. The modules were combined into an integrated package of EOR preliminary assessment. Finally, we utilised the toolkit to evaluate several Indonesian oil fields for EOR evaluation (past projects) and feasibility (future projects). The attempt was able to update the previous consideration regarding EOR attractiveness and open new opportunity for EOR implementation in Indonesia.

  10. Prediction of leisure-time walking: an integration of social cognitive, perceived environmental, and personality factors

    Directory of Open Access Journals (Sweden)

    Blanchard Chris M

    2007-10-01

    Full Text Available Abstract Background Walking is the primary focus of population-based physical activity initiatives but a theoretical understanding of this behaviour is still elusive. The purpose of this study was to integrate personality, the perceived environment, and planning into a theory of planned behaviour (TPB framework to predict leisure-time walking. Methods Participants were a random sample (N = 358 of Canadian adults who completed measures of the TPB, planning, perceived neighbourhood environment, and personality at Time 1 and self-reported walking behaviour two months later. Results Analyses using structural equation modelling provided evidence that leisure-time walking is largely predicted by intention (standardized effect = .42 with an additional independent contribution from proximity to neighbourhood retail shops (standardized effect = .18. Intention, in turn, was predicted by attitudes toward walking and perceived behavioural control. Effects of perceived neighbourhood aesthetics and walking infrastructure on walking were mediated through attitudes and intention. Moderated regression analysis showed that the intention-walking relationship was moderated by conscientiousness and proximity to neighbourhood recreation facilities but not planning. Conclusion Overall, walking behaviour is theoretically complex but may best be addressed at a population level by facilitating strong intentions in a receptive environment even though individual differences may persist.

  11. An integrated computational validation approach for potential novel miRNA prediction

    Directory of Open Access Journals (Sweden)

    Pooja Viswam

    2017-12-01

    Full Text Available MicroRNAs (miRNAs are short, non-coding RNAs between 17bp-24bp length that regulate gene expression by targeting mRNA molecules. The regulatory functions of miRNAs are known to be majorly associated with disease phenotypes such as cancer, cell signaling, cell division, growth and other metabolisms. Novel miRNAs are defined as sequences which does not have any similarity with the existing known sequences and void of any experimental evidences. In recent decades, the advent of next-generation sequencing allows us to capture the small RNA molecules form the cells and developing methods to estimate their expression levels. Several computational algorithms are available to predict the novel miRNAs from the deep sequencing data. In this work, we integrated three novel miRNA prediction programs miRDeep, miRanalyzer and miRPRo to compare and validate their prediction efficiency. The dicer cleavage sites, alignment density, seed conservation, minimum free energy, AU-GC percentage, secondary loop scores, false discovery rates and confidence scores will be considered for comparison and evaluation. Efficiency to identify isomiRs and base pair mismatches in a strand specific manner will also be considered for the computational validation. Further, the criteria and parameters for the identification of the best possible novel miRNA with minimal false positive rates were deduced.

  12. Performance prediction for silicon photonics integrated circuits with layout-dependent correlated manufacturing variability.

    Science.gov (United States)

    Lu, Zeqin; Jhoja, Jaspreet; Klein, Jackson; Wang, Xu; Liu, Amy; Flueckiger, Jonas; Pond, James; Chrostowski, Lukas

    2017-05-01

    This work develops an enhanced Monte Carlo (MC) simulation methodology to predict the impacts of layout-dependent correlated manufacturing variations on the performance of photonics integrated circuits (PICs). First, to enable such performance prediction, we demonstrate a simple method with sub-nanometer accuracy to characterize photonics manufacturing variations, where the width and height for a fabricated waveguide can be extracted from the spectral response of a racetrack resonator. By measuring the spectral responses for a large number of identical resonators spread over a wafer, statistical results for the variations of waveguide width and height can be obtained. Second, we develop models for the layout-dependent enhanced MC simulation. Our models use netlist extraction to transfer physical layouts into circuit simulators. Spatially correlated physical variations across the PICs are simulated on a discrete grid and are mapped to each circuit component, so that the performance for each component can be updated according to its obtained variations, and therefore, circuit simulations take the correlated variations between components into account. The simulation flow and theoretical models for our layout-dependent enhanced MC simulation are detailed in this paper. As examples, several ring-resonator filter circuits are studied using the developed enhanced MC simulation, and statistical results from the simulations can predict both common-mode and differential-mode variations of the circuit performance.

  13. Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation

    KAUST Repository

    Hartono, A. D.

    2017-10-17

    EOR preliminary analysis is pivotal to be performed at early stage of assessment in order to elucidate EOR feasibility. This study proposes an in-depth analysis toolkit for EOR preliminary evaluation. The toolkit incorporates EOR screening, predictive, economic, risk analysis and optimisation modules. The screening module introduces algorithms which assimilates statistical and engineering notions into consideration. The United States Department of Energy (U.S. DOE) predictive models were implemented in the predictive module. The economic module is available to assess project attractiveness, while Monte Carlo Simulation is applied to quantify risk and uncertainty of the evaluated project. Optimization scenario of EOR practice can be evaluated using the optimisation module, in which stochastic methods of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES) were applied in the algorithms. The modules were combined into an integrated package of EOR preliminary assessment. Finally, we utilised the toolkit to evaluate several Indonesian oil fields for EOR evaluation (past projects) and feasibility (future projects). The attempt was able to update the previous consideration regarding EOR attractiveness and open new opportunity for EOR implementation in Indonesia.

  14. Integration of relational and hierarchical network information for protein function prediction

    Directory of Open Access Journals (Sweden)

    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  15. Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.

    Directory of Open Access Journals (Sweden)

    Meeshanthini V Dogan

    Full Text Available An improved method for detecting coronary heart disease (CHD could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

  16. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

    Science.gov (United States)

    Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

    2017-12-21

    In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

  17. Rigorous assessment and integration of the sequence and structure based features to predict hot spots

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-07-01

    classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.

  18. Prediction of the flooding process at the Ronneburg site - results of an integrated approach

    International Nuclear Information System (INIS)

    Paul, M.; Saenger, H.-J.; Snagowski, S.; Maerten, H.; Eckart, M.

    1998-01-01

    The flooding process of the Ronneburg uranium mine (WISMUT) was initiated at the turn of the year 1997 to 1998. In order to prepare the flooding process and to derive and optimize technological measures an integrated modelling approach was chosen which includes several coupled modules. The most important issues to be answered are: (1) prediction of the flooding time (2) prediction of the groundwater level at the post-flooding stage, assessment of amount, location and quality of flooding waters entering the receiving streams at the final stage (3) water quality prediction within the mine during the flooding process (4) definition of technological measures and assessment of their efficiency A box model which includes the three-dimensional distribution of the cavity volume in the mine represents the model core. The model considers the various types of dewatered cavity volumes for each mine level / mining field and the degree of vertical and horizontal connection between the mining fields. Different types of open mine space as well as the dewatered geological pore and joint volume are considered taking into account the contour of the depression cone prior to flooding and the characteristics of the different rock types. Based on the mine water balance and the flooding technology the model predicts the rise of the water table over time during the flooding process for each mine field separately. In order to predict the mine water quality and the efficiency of in-situ water treatment the box model was linked to a geochemical model (PHREEQC). A three-dimensional flow model is used to evaluate the post-flooding situation at the Ronneburg site. This model is coupled to the box model. The modelling results of various flooding scenarios show that a prediction of the post-flooding geohydraulic situation is possible despite of uncertainties concerning the input parameters which still exist. The post-flooding water table in the central part of the Ronneburg mine will be 270 m

  19. Rigorous assessment and integration of the sequence and structure based features to predict hot spots

    Science.gov (United States)

    2011-01-01

    effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots. PMID:21798070

  20. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    International Nuclear Information System (INIS)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

    2014-01-01

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval

  1. Behaviors of impurity in ITER and DEMOs using BALDUR integrated predictive modeling code

    International Nuclear Information System (INIS)

    Onjun, Thawatchai; Buangam, Wannapa; Wisitsorasak, Apiwat

    2015-01-01

    The behaviors of impurity are investigated using self-consistent modeling of 1.5D BALDUR integrated predictive modeling code, in which theory-based models are used for both core and edge region. In these simulations, a combination of NCLASS neoclassical transport and Multi-mode (MMM95) anomalous transport model is used to compute a core transport. The boundary is taken to be at the top of the pedestal, where the pedestal values are described using a theory-based pedestal model. This pedestal temperature model is based on a combination of magnetic and flow shear stabilization pedestal width scaling and an infinite-n ballooning pressure gradient model. The time evolution of plasma current, temperature and density profiles is carried out for ITER and DEMOs plasmas. As a result, the impurity behaviors such as impurity accumulation and impurity transport can be investigated. (author)

  2. A model of integration among prediction tools: applied study to road freight transportation

    Directory of Open Access Journals (Sweden)

    Henrique Dias Blois

    Full Text Available Abstract This study has developed a scenery analysis model which has integrated decision-making tools on investments: prospective scenarios (Grumbach Method and systems dynamics (hard modeling, with the innovated multivariate analysis of experts. It was designed through analysis and simulation scenarios and showed which are the most striking events in the study object as well as highlighted the actions could redirect the future of the analyzed system. Moreover, predictions are likely to be developed through the generated scenarios. The model has been validated empirically with road freight transport data from state of Rio Grande do Sul, Brazil. The results showed that the model contributes to the analysis of investment because it identifies probabilities of events that impact on decision making, and identifies priorities for action, reducing uncertainties in the future. Moreover, it allows an interdisciplinary discussion that correlates different areas of knowledge, fundamental when you wish more consistency in creating scenarios.

  3. Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction.

    Science.gov (United States)

    Velderraín, José Dávila; Martínez-García, Juan Carlos; Álvarez-Buylla, Elena R

    2017-01-01

    Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

  4. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    Science.gov (United States)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

    2014-09-01

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

  5. An integrated numerical model for the prediction of Gaussian and billet shapes

    International Nuclear Information System (INIS)

    Hattel, J.H.; Pryds, N.H.; Pedersen, T.B.

    2004-01-01

    Separate models for the atomisation and the deposition stages were recently integrated by the authors to form a unified model describing the entire spray-forming process. In the present paper, the focus is on describing the shape of the deposited material during the spray-forming process, obtained by this model. After a short review of the models and their coupling, the important factors which influence the resulting shape, i.e. Gaussian or billet, are addressed. The key parameters, which are utilized to predict the geometry and dimension of the deposited material, are the sticking efficiency and the shading effect for Gaussian and billet shape, respectively. From the obtained results, the effect of these parameters on the final shape is illustrated

  6. Model predictive control system and method for integrated gasification combined cycle power generation

    Science.gov (United States)

    Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

    2013-04-09

    Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

  7. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    Energy Technology Data Exchange (ETDEWEB)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)

    2014-09-12

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

  8. Integrating the ICF with positive psychology: Factors predicting role participation for mothers with multiple sclerosis.

    Science.gov (United States)

    Farber, Ruth S; Kern, Margaret L; Brusilovsky, Eugene

    2015-05-01

    Being a mother has become a realizable life role for women with disabilities and chronic illnesses, including multiple sclerosis (MS). Identifying psychosocial factors that facilitate participation in important life roles-including motherhood-is essential to help women have fuller lives despite the challenge of their illness. By integrating the International Classification of Functioning, Disability, and Health (ICF) and a positive psychology perspective, this study examined how environmental social factors and positive personal factors contribute to daily role participation and satisfaction with parental participation. One hundred and 11 community-dwelling mothers with MS completed Ryff's Psychological Well-Being Scales, the Medical Outcome Study Social Support Survey, the Short Form-36, and the Parental Participation Scale. Hierarchical regression analyses examined associations between social support and positive personal factors (environmental mastery, self-acceptance, purpose in life) with daily role participation (physical and emotional) and satisfaction with parental participation. One-way ANOVAs tested synergistic combinations of social support and positive personal factors. Social support predicted daily role participation (fewer limitations) and greater satisfaction with parental participation. Positive personal factors contributed additional unique variance. Positive personal factors and social support synergistically predicted better function and greater satisfaction than either alone. Integrating components of the ICF and positive psychology provides a useful model for understanding how mothers with MS can thrive despite challenge or impairment. Both positive personal factors and environmental social factors were important contributors to positive role functioning. Incorporating these paradigms into treatment may help mothers with MS participate more fully in meaningful life roles. (c) 2015 APA, all rights reserved).

  9. Reward and Cognition: Integrating Reinforcement Sensitivity Theory and Social Cognitive Theory to Predict Drinking Behavior.

    Science.gov (United States)

    Hasking, Penelope; Boyes, Mark; Mullan, Barbara

    2015-01-01

    Both Reinforcement Sensitivity Theory and Social Cognitive Theory have been applied to understanding drinking behavior. We propose that theoretical relationships between these models support an integrated approach to understanding alcohol use and misuse. We aimed to test an integrated model in which the relationships between reward sensitivity and drinking behavior (alcohol consumption, alcohol-related problems, and symptoms of dependence) were mediated by alcohol expectancies and drinking refusal self-efficacy. Online questionnaires assessing the constructs of interest were completed by 443 Australian adults (M age = 26.40, sd = 1.83) in 2013 and 2014. Path analysis revealed both direct and indirect effects and implicated two pathways to drinking behavior with differential outcomes. Drinking refusal self-efficacy both in social situations and for emotional relief was related to alcohol consumption. Sensitivity to reward was associated with alcohol-related problems, but operated through expectations of increased confidence and personal belief in the ability to limit drinking in social situations. Conversely, sensitivity to punishment operated through negative expectancies and drinking refusal self-efficacy for emotional relief to predict symptoms of dependence. Two pathways relating reward sensitivity, alcohol expectancies, and drinking refusal self-efficacy may underlie social and dependent drinking, which has implications for development of intervention to limit harmful drinking.

  10. An integrative typology of personality assessment for aggression: implications for predicting counterproductive workplace behavior.

    Science.gov (United States)

    Bing, Mark N; Stewart, Susan M; Davison, H Kristl; Green, Philip D; McIntyre, Michael D; James, Lawrence R

    2007-05-01

    This study presents an integrative typology of personality assessment for aggression. In this typology, self-report and conditional reasoning (L. R. James, 1998) methodologies are used to assess 2 separate, yet often congruent, components of aggressive personalities. Specifically, self-report is used to assess explicit components of aggressive tendencies, such as self-perceived aggression, whereas conditional reasoning is used to assess implicit components, in particular, the unconscious biases in reasoning that are used to justify aggressive acts. These 2 separate components are then integrated to form a new theoretical typology of personality assessment for aggression. Empirical tests of the typology were subsequently conducted using data gathered across 3 samples in laboratory and field settings and reveal that explicit and implicit components of aggression can interact in the prediction of counterproductive, deviant, and prosocial behaviors. These empirical tests also reveal that when either the self-report or conditional reasoning methodology is used in isolation, the resulting assessment of aggression may be incomplete. Implications for personnel selection, team composition, and executive coaching are discussed. 2007 APA, all rights reserved

  11. Integrated predictive modeling simulations of the Mega-Amp Spherical Tokamak

    International Nuclear Information System (INIS)

    Nguyen, Canh N.; Bateman, Glenn; Kritz, Arnold H.; Akers, Robert; Byrom, Calum; Sykes, Alan

    2002-01-01

    Integrated predictive modeling simulations are carried out using the BALDUR transport code [Singer et al., Comput. Phys. Commun. 49, 275 (1982)] for high confinement mode (H-mode) and low confinement mode (L-mode) discharges in the Mega-Amp Spherical Tokamak (MAST) [Sykes et al., Phys. Plasmas 8, 2101 (2001)]. Simulation results, obtained using either the Multi-Mode transport model (MMM95) or, alternatively, the mixed-Bohm/gyro-Bohm transport model, are compared with experimental data. In addition to the anomalous transport, neoclassical transport is included in the simulations and the ion thermal diffusivity in the inner third of the plasma is found to be predominantly neoclassical. The sawtooth oscillations in the simulations radially spread the neutral beam injection heating profiles across a broad sawtooth mixing region. The broad sawtooth oscillations also flatten the central temperature and electron density profiles. Simulation results for the electron temperature and density profiles are compared with experimental data to test the applicability of these models and the BALDUR integrated modeling code in the limit of low aspect ratio toroidal plasmas

  12. Using NCAP to predict RFI effects in linear bipolar integrated circuits

    Science.gov (United States)

    Fang, T.-F.; Whalen, J. J.; Chen, G. K. C.

    1980-11-01

    Applications of the Nonlinear Circuit Analysis Program (NCAP) to calculate RFI effects in electronic circuits containing discrete semiconductor devices have been reported upon previously. The objective of this paper is to demonstrate that the computer program NCAP also can be used to calcuate RFI effects in linear bipolar integrated circuits (IC's). The IC's reported upon are the microA741 operational amplifier (op amp) which is one of the most widely used IC's, and a differential pair which is a basic building block in many linear IC's. The microA741 op amp was used as the active component in a unity-gain buffer amplifier. The differential pair was used in a broad-band cascode amplifier circuit. The computer program NCAP was used to predict how amplitude-modulated RF signals are demodulated in the IC's to cause undesired low-frequency responses. The predicted and measured results for radio frequencies in the 0.050-60-MHz range are in good agreement.

  13. Integrated Design Software Predicts the Creep Life of Monolithic Ceramic Components

    Science.gov (United States)

    1996-01-01

    Significant improvements in propulsion and power generation for the next century will require revolutionary advances in high-temperature materials and structural design. Advanced ceramics are candidate materials for these elevated-temperature applications. As design protocols emerge for these material systems, designers must be aware of several innate features, including the degrading ability of ceramics to carry sustained load. Usually, time-dependent failure in ceramics occurs because of two different, delayedfailure mechanisms: slow crack growth and creep rupture. Slow crack growth initiates at a preexisting flaw and continues until a critical crack length is reached, causing catastrophic failure. Creep rupture, on the other hand, occurs because of bulk damage in the material: void nucleation and coalescence that eventually leads to macrocracks which then propagate to failure. Successful application of advanced ceramics depends on proper characterization of material behavior and the use of an appropriate design methodology. The life of a ceramic component can be predicted with the NASA Lewis Research Center's Ceramics Analysis and Reliability Evaluation of Structures (CARES) integrated design programs. CARES/CREEP determines the expected life of a component under creep conditions, and CARES/LIFE predicts the component life due to fast fracture and subcritical crack growth. The previously developed CARES/LIFE program has been used in numerous industrial and Government applications.

  14. MirZ: an integrated microRNA expression atlas and target prediction resource.

    Science.gov (United States)

    Hausser, Jean; Berninger, Philipp; Rodak, Christoph; Jantscher, Yvonne; Wirth, Stefan; Zavolan, Mihaela

    2009-07-01

    MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at www.mirz.unibas.ch. The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapiens.

  15. Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

    Science.gov (United States)

    Wilkin, John L.; Rosenfeld, Leslie; Allen, Arthur; Baltes, Rebecca; Baptista, Antonio; He, Ruoying; Hogan, Patrick; Kurapov, Alexander; Mehra, Avichal; Quintrell, Josie; Schwab, David; Signell, Richard; Smith, Jane

    2017-01-01

    This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation, and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modelling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3–8-year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, US Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal–academic partnerships benefiting IOOS stakeholders and end users.

  16. Integrating genomics and proteomics data to predict drug effects using binary linear programming.

    Science.gov (United States)

    Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

    2014-01-01

    The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be

  17. Integrating Unified Gravity Wave Physics into the NOAA Next Generation Global Prediction System

    Science.gov (United States)

    Alpert, J. C.; Yudin, V.; Fuller-Rowell, T. J.; Akmaev, R. A.

    2017-12-01

    The Unified Gravity Wave Physics (UGWP) project for the Next Generation Global Prediction System (NGGPS) is a NOAA collaborative effort between the National Centers for Environmental Prediction (NCEP), Environemntal Modeling Center (EMC) and the University of Colorado, Cooperative Institute for Research in Environmental Sciences (CU-CIRES) to support upgrades and improvements of GW dynamics (resolved scales) and physics (sub-grid scales) in the NOAA Environmental Modeling System (NEMS)†. As envisioned the global climate, weather and space weather models of NEMS will substantially improve their predictions and forecasts with the resolution-sensitive (scale-aware) formulations planned under the UGWP framework for both orographic and non-stationary waves. In particular, the planned improvements for the Global Forecast System (GFS) model of NEMS are: calibration of model physics for higher vertical and horizontal resolution and an extended vertical range of simulations, upgrades to GW schemes, including the turbulent heating and eddy mixing due to wave dissipation and breaking, and representation of the internally-generated QBO. The main priority of the UGWP project is unified parameterization of orographic and non-orographic GW effects including momentum deposition in the middle atmosphere and turbulent heating and eddies due to wave dissipation and breaking. The latter effects are not currently represented in NOAA atmosphere models. The team has tested and evaluated four candidate GW solvers integrating the selected GW schemes into the NGGPS model. Our current work and planned activity is to implement the UGWP schemes in the first available GFS/FV3 (open FV3) configuration including adapted GFDL modification for sub-grid orography in GFS. Initial global model results will be shown for the operational and research GFS configuration for spectral and FV3 dynamical cores. †http://www.emc.ncep.noaa.gov/index.php?branch=NEMS

  18. Prediction of irradiation damage effects by multi-scale modelling: EURATOM 3 Framework integrated project perfect

    International Nuclear Information System (INIS)

    Massoud, J.P.; Bugat, St.; Marini, B.; Lidbury, D.; Van Dyck, St.; Debarberis, L.

    2008-01-01

    Full text of publication follows. In nuclear PWRs, materials undergo degradation due to severe irradiation conditions that may limit their operational life. Utilities operating these reactors must quantify the aging and the potential degradations of reactor pressure vessels and also of internal structures to ensure safe and reliable plant operation. The EURATOM 6. Framework Integrated Project PERFECT (Prediction of Irradiation Damage Effects in Reactor Components) addresses irradiation damage in RPV materials and components by multi-scale modelling. This state-of-the-art approach offers potential advantages over the conventional empirical methods used in current practice of nuclear plant lifetime management. Launched in January 2004, this 48-month project is focusing on two main components of nuclear power plants which are subject to irradiation damage: the ferritic steel reactor pressure vessel and the austenitic steel internals. This project is also an opportunity to integrate the fragmented research and experience that currently exists within Europe in the field of numerical simulation of radiation damage and creates the links with international organisations involved in similar projects throughout the world. Continuous progress in the physical understanding of the phenomena involved in irradiation damage and continuous progress in computer sciences make possible the development of multi-scale numerical tools able to simulate the effects of irradiation on materials microstructure. The consequences of irradiation on mechanical and corrosion properties of materials are also tentatively modelled using such multi-scale modelling. But it requires to develop different mechanistic models at different levels of physics and engineering and to extend the state of knowledge in several scientific fields. And the links between these different kinds of models are particularly delicate to deal with and need specific works. Practically the main objective of PERFECT is to build

  19. Frequent and recent retrotransposition of orthologous genes plays a role in the evolution of sperm glycolytic enzymes

    Directory of Open Access Journals (Sweden)

    de Villena Fernando

    2010-05-01

    Full Text Available Abstract Background The central metabolic pathway of glycolysis converts glucose to pyruvate, with the net production of 2 ATP and 2 NADH per glucose molecule. Each of the ten reactions in this pathway is typically catalyzed by multiple isozymes encoded by a multigene family. Several isozymes in this pathway are expressed only during spermatogenesis, and gene targeting studies indicate that they are essential for sperm function and male fertility in mouse. At least three of the novel glycolytic isozymes are encoded by retrogenes (Pgk2, Aldoart1, and Aldoart2. Their restricted expression profile suggests that retrotransposition may play a significant role in the evolution of sperm glycolytic enzymes. Results We conducted a comprehensive genomic analysis of glycolytic enzymes in the human and mouse genomes and identified several intronless copies for all enzymes in the pathway, except Pfk. Within each gene family, a single orthologous gene was typically retrotransposed frequently and independently in both species. Several retroposed sequences maintained open reading frames (ORFs and/or provided evidence of alternatively spliced exons. We analyzed expression of sequences with ORFs and Gpi1 transcript in mouse spermatogenic cells. Conclusions Our analysis detected frequent, recent, and lineage-specific retrotransposition of orthologous glycolytic enzymes in the human and mouse genomes. Retrotransposition events are associated with LINE/LTR and genomic integration is random. We found evidence for the alternative splicing of parent genes. Many retroposed sequences have maintained ORFs, suggesting a functional role for these genes.

  20. TaWRKY68 responses to biotic stresses are revealed by the orthologous genes from major cereals

    Directory of Open Access Journals (Sweden)

    Bo Ding

    2014-01-01

    Full Text Available WRKY transcription factors have been extensively characterized in the past 20 years, but in wheat, studies onWRKY genes and their function are lagging behind many other species. To explore the function of wheat WRKY genes, we identified a TaWRKY68 gene from a common wheat cultivar. It encodes a protein comprising 313 amino acids which harbors 19 conserved motifs or active sites. Gene expression patterns were determined by analyzing microarray data of TaWRKY68 in wheat and of orthologous genes from maize, rice and barley using Genevestigator. TaWRKY68 orthologs were identified and clustered using DELTA-BLAST and COBALT programs available at NCBI. The results showed that these genes, which are expressed in all tissues tested, had relatively higher levels in the roots and were up-regulated in response to biotic stresses. Bioinformatics results were confirmed by RT-PCR experiments using wheat plants infected by Agrobacterium tumefaciens and Blumeria graminis, or treated with Deoxynivalenol, a Fusarium graminearum-induced mycotoxin in wheat or barley. In summary,TaWRKY68 functions differ during plant developmental stages and might be representing a hub gene function in wheat responses to various biotic stresses. It was also found that including data from major cereal genes in the bioinformatics analysis gave more accurate and comprehensive predictions of wheat gene functions.

  1. The Princeton Protein Orthology Database (P-POD): a comparative genomics analysis tool for biologists.

    OpenAIRE

    Sven Heinicke; Michael S Livstone; Charles Lu; Rose Oughtred; Fan Kang; Samuel V Angiuoli; Owen White; David Botstein; Kara Dolinski

    2007-01-01

    Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic r...

  2. Cis-regulatory signatures of orthologous stress-associated bZIP transcription factors from rice, sorghum and Arabidopsis based on phylogenetic footprints

    Directory of Open Access Journals (Sweden)

    Xu Fuyu

    2012-09-01

    Full Text Available Abstract Background The potential contribution of upstream sequence variation to the unique features of orthologous genes is just beginning to be unraveled. A core subset of stress-associated bZIP transcription factors from rice (Oryza sativa formed ten clusters of orthologous groups (COG with genes from the monocot sorghum (Sorghum bicolor and dicot Arabidopsis (Arabidopsis thaliana. The total cis-regulatory information content of each stress-associated COG was examined by phylogenetic footprinting to reveal ortholog-specific, lineage-specific and species-specific conservation patterns. Results The most apparent pattern observed was the occurrence of spatially conserved ‘core modules’ among the COGs but not among paralogs. These core modules are comprised of various combinations of two to four putative transcription factor binding site (TFBS classes associated with either developmental or stress-related functions. Outside the core modules are specific stress (ABA, oxidative, abiotic, biotic or organ-associated signals, which may be functioning as ‘regulatory fine-tuners’ and further define lineage-specific and species-specific cis-regulatory signatures. Orthologous monocot and dicot promoters have distinct TFBS classes involved in disease and oxidative-regulated expression, while the orthologous rice and sorghum promoters have distinct combinations of root-specific signals, a pattern that is not particularly conserved in Arabidopsis. Conclusions Patterns of cis-regulatory conservation imply that each ortholog has distinct signatures, further suggesting that they are potentially unique in a regulatory context despite the presumed conservation of broad biological function during speciation. Based on the observed patterns of conservation, we postulate that core modules are likely primary determinants of basal developmental programming, which may be integrated with and further elaborated by additional intrinsic or extrinsic signals in

  3. The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

    Science.gov (United States)

    Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G., Jr.

    2015-01-01

    The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting

  4. The Integrated Medical Model: A Probabilistic Simulation Model for Predicting In-Flight Medical Risks

    Science.gov (United States)

    Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G.

    2015-01-01

    The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting

  5. Predicting Teacher Participation in a Classroom-Based, Integrated Preventive Intervention for Preschoolers.

    Science.gov (United States)

    Baker, Courtney N; Kupersmidt, Janis B; Voegler-Lee, Mary Ellen; Arnold, David H; Willoughby, Michael T

    2010-01-01

    Preschools provide a promising setting in which to conduct preventive interventions for childhood problems, but classroom programs can only be effective if teachers are willing and able to implement them. This study is one of the first to investigate predictors of the frequency of teacher participation in a classroom-based, randomized controlled trial of an integrated prevention program for preschoolers. The intervention was designed to promote school readiness with an integrated social and academic program, to be implemented by teachers with the support of classroom consultants. The current study is part of a larger project conducted with Head Start and community child care centers that serve primarily economically disadvantaged families; 49 teachers from 30 centers participated in this study. Overall, teachers conducted approximately 70% of the program activities. Participation decreased significantly over time from the first to the final week of the intervention, and also decreased within each week of the intervention, from the first to the final weekly activity. Teachers working at community child care centers implemented more intervention activities than did Head Start teachers. Teacher concerns about the intervention, assessed prior to training, predicted less participation. In addition, teachers' participation was positively related to their perception that their centers and directors were supportive, collegial, efficient, and fair, as well as their job satisfaction and commitment. Teacher experience, education, ethnicity, and self-efficacy were not significantly related to participation. In multi-level models that considered center as a level of analysis, substantial variance was accounted for by centers, pointing to the importance of considering center-level predictors in future research.

  6. Morphogenesis of Strongyloides stercoralis infective larvae requires the DAF-16 ortholog FKTF-1.

    Directory of Open Access Journals (Sweden)

    Michelle L Castelletto

    2009-04-01

    Full Text Available Based on metabolic and morphological similarities between infective third-stage larvae of parasitic nematodes and dauer larvae of Caenorhabditis elegans, it is hypothesized that similar genetic mechanisms control the development of these forms. In the parasite Strongyloides stercoralis, FKTF-1 is an ortholog of DAF-16, a forkhead transcription factor that regulates dauer larval development in C. elegans. Using transgenesis, we investigated the role of FKTF-1 in S. stercoralis' infective larval development. In first-stage larvae, GFP-tagged recombinant FKTF-1b localizes to the pharynx and hypodermis, tissues remodeled in infective larvae. Activating and inactivating mutations at predicted AKT phosphorylation sites on FKTF-1b give constitutive cytoplasmic and nuclear localization of the protein, respectively, indicating that its post-translational regulation is similar to other FOXO-class transcription factors. Mutant constructs designed to interfere with endogenous FKTF-1b function altered the intestinal and pharyngeal development of the larvae and resulted in some transgenic larvae failing to arrest in the infective stage. Our findings indicate that FKTF-1b is required for proper morphogenesis of S. stercoralis infective larvae and support the overall hypothesis of similar regulation of dauer development in C. elegans and the formation of infective larvae in parasitic nematodes.

  7. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

    Science.gov (United States)

    Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

    2017-09-18

    The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

  8. The dynamical integrity concept for interpreting/ predicting experimental behaviour: from macro- to nano-mechanics.

    Science.gov (United States)

    Lenci, Stefano; Rega, Giuseppe; Ruzziconi, Laura

    2013-06-28

    The dynamical integrity, a new concept proposed by J.M.T. Thompson, and developed by the authors, is used to interpret experimental results. After reviewing the main issues involved in this analysis, including the proposal of a new integrity measure able to capture in an easy way the safe part of basins, attention is dedicated to two experiments, a rotating pendulum and a micro-electro-mechanical system, where the theoretical predictions are not fulfilled. These mechanical systems, the former at the macro-scale and the latter at the micro-scale, permit a comparative analysis of different mechanical and dynamical behaviours. The fact that in both cases the dynamical integrity permits one to justify the difference between experimental and theoretical results, which is the main achievement of this paper, shows the effectiveness of this new approach and suggests its use in practical situations. The men of experiment are like the ant, they only collect and use; the reasoners resemble spiders, who make cobwebs out of their own substance. But the bee takes the middle course: it gathers its material from the flowers of the garden and field, but transforms and digests it by a power of its own. Not unlike this is the true business of philosophy (science); for it neither relies solely or chiefly on the powers of the mind, nor does it take the matter which it gathers from natural history and mechanical experiments and lay up in the memory whole, as it finds it, but lays it up in the understanding altered and digested. Therefore, from a closer and purer league between these two faculties, the experimental and the rational (such as has never been made), much may be hoped. (Francis Bacon 1561-1626) But are we sure of our observational facts? Scientific men are rather fond of saying pontifically that one ought to be quite sure of one's observational facts before embarking on theory. Fortunately those who give this advice do not practice what they preach. Observation and theory get

  9. Integrated and Total HIV-1 DNA Predict Ex Vivo Viral Outgrowth.

    Directory of Open Access Journals (Sweden)

    Maja Kiselinova

    2016-03-01

    Full Text Available The persistence of a reservoir of latently infected CD4 T cells remains one of the major obstacles to cure HIV. Numerous strategies are being explored to eliminate this reservoir. To translate these efforts into clinical trials, there is a strong need for validated biomarkers that can monitor the reservoir over time in vivo. A comprehensive study was designed to evaluate and compare potential HIV-1 reservoir biomarkers. A cohort of 25 patients, treated with suppressive antiretroviral therapy was sampled at three time points, with median of 2.5 years (IQR: 2.4-2.6 between time point 1 and 2; and median of 31 days (IQR: 28-36 between time point 2 and 3. Patients were median of 6 years (IQR: 3-12 on ART, and plasma viral load (<50 copies/ml was suppressed for median of 4 years (IQR: 2-8. Total HIV-1 DNA, unspliced (us and multiply spliced HIV-1 RNA, and 2LTR circles were quantified by digital PCR in peripheral blood, at 3 time points. At the second time point, a viral outgrowth assay (VOA was performed, and integrated HIV-1 DNA and relative mRNA expression levels of HIV-1 restriction factors were quantified. No significant change was found for long- and short-term dynamics of all HIV-1 markers tested in peripheral blood. Integrated HIV-1 DNA was associated with total HIV-1 DNA (p<0.001, R² = 0.85, us HIV-1 RNA (p = 0.029, R² = 0.40, and VOA (p = 0.041, R2 = 0.44. Replication-competent virus was detected in 80% of patients by the VOA and it correlated with total HIV-1 DNA (p = 0.039, R² = 0.54. The mean quantification difference between Alu-PCR and VOA was 2.88 log10, and 2.23 log10 between total HIV-1 DNA and VOA. The levels of usHIV-1 RNA were inversely correlated with mRNA levels of several HIV-1 restriction factors (TRIM5α, SAMHD1, MX2, SLFN11, pSIP1. Our study reveals important correlations between the viral outgrowth and total and integrated HIV-1 DNA measures, suggesting that the total pool of HIV-1 DNA may predict the size of the

  10. Predict-first experimental analysis using automated and integrated magnetohydrodynamic modeling

    Science.gov (United States)

    Lyons, B. C.; Paz-Soldan, C.; Meneghini, O.; Lao, L. L.; Weisberg, D. B.; Belli, E. A.; Evans, T. E.; Ferraro, N. M.; Snyder, P. B.

    2018-05-01

    An integrated-modeling workflow has been developed for the purpose of performing predict-first analysis of transient-stability experiments. Starting from an existing equilibrium reconstruction from a past experiment, the workflow couples together the EFIT Grad-Shafranov solver [L. Lao et al., Fusion Sci. Technol. 48, 968 (2005)], the EPED model for the pedestal structure [P. B. Snyder et al., Phys. Plasmas 16, 056118 (2009)], and the NEO drift-kinetic-equation solver [E. A. Belli and J. Candy, Plasma Phys. Controlled Fusion 54, 015015 (2012)] (for bootstrap current calculations) in order to generate equilibria with self-consistent pedestal structures as the plasma shape and various scalar parameters (e.g., normalized β, pedestal density, and edge safety factor [q95]) are changed. These equilibria are then analyzed using automated M3D-C1 extended-magnetohydrodynamic modeling [S. C. Jardin et al., Comput. Sci. Discovery 5, 014002 (2012)] to compute the plasma response to three-dimensional magnetic perturbations. This workflow was created in conjunction with a DIII-D experiment examining the effect of triangularity on the 3D plasma response. Several versions of the workflow were developed, and the initial ones were used to help guide experimental planning (e.g., determining the plasma current necessary to maintain the constant edge safety factor in various shapes). Subsequent validation with the experimental results was then used to revise the workflow, ultimately resulting in the complete model presented here. We show that quantitative agreement was achieved between the M3D-C1 plasma response calculated for equilibria generated by the final workflow and equilibria reconstructed from experimental data. A comparison of results from earlier workflows is used to show the importance of properly matching certain experimental parameters in the generated equilibria, including the normalized β, pedestal density, and q95. On the other hand, the details of the pedestal

  11. An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer.

    Directory of Open Access Journals (Sweden)

    Kelly H Salter

    Full Text Available A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective.Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%. When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06. Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8% of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04, representing a viable alternative therapy.Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding micro

  12. A Study of Performance in Low-Power Tokamak Reactor with Integrated Predictive Modeling Code

    International Nuclear Information System (INIS)

    Pianroj, Y.; Onjun, T.; Suwanna, S.; Picha, R.; Poolyarat, N.

    2009-07-01

    Full text: A fusion hybrid or a small fusion power output with low power tokamak reactor is presented as another useful application of nuclear fusion. Such tokamak can be used for fuel breeding, high-level waste transmutation, hydrogen production at high temperature, and testing of nuclear fusion technology components. In this work, an investigation of the plasma performance in a small fusion power output design is carried out using the BALDUR predictive integrated modeling code. The simulations of the plasma performance in this design are carried out using the empirical-based Mixed Bohm/gyro Bohm (B/gB) model, whereas the pedestal temperature model is based on magnetic and flow shear (δ α ρ ζ 2 ) stabilization pedestal width scaling. The preliminary results using this core transport model show that the central ion and electron temperatures are rather pessimistic. To improve the performance, the optimization approach are carried out by varying some parameters, such as plasma current and power auxiliary heating, which results in some improvement of plasma performance

  13. Predicting body appreciation in young women: An integrated model of positive body image.

    Science.gov (United States)

    Andrew, Rachel; Tiggemann, Marika; Clark, Levina

    2016-09-01

    This study examined a range of predictors, based on previous theoretical models, of positive body image in young adult women. Participants were 266 women who completed an online questionnaire measuring body appreciation, activity participation, media consumption, perceived body acceptance by others, self-compassion, and autonomy. Potential mechanisms in predicting body appreciation assessed were self-objectification, social appearance comparison, and thin-ideal internalisation. Results indicated that greater perceived body acceptance by others and self-compassion, and lower appearance media consumption, self-objectification, social comparison, and thin-ideal internalisation were related to greater body appreciation. An integrated model showed that appearance media (negatively) and non-appearance media and self-compassion (positively) were associated with lower self-objectification, social comparison, and thin-ideal internalisation, which in turn related to greater body appreciation. Additionally, perceived body acceptance by others was directly associated with body appreciation. The results contribute to an understanding of potential pathways of positive body image development, thereby highlighting possible intervention targets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

    Directory of Open Access Journals (Sweden)

    Shokri Saeid

    2015-01-01

    Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.

  15. A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

    Science.gov (United States)

    Louis R. Iverson; Martin E. Dale; Charles T. Scott; Anantha Prasad; Anantha Prasad

    1997-01-01

    A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position...

  16. Analytical solutions for prediction of the ignition time of wood particles based on a time and space integral method

    NARCIS (Netherlands)

    Haseli, Y.; Oijen, van J.A.; Goey, de L.P.H.

    2012-01-01

    The main idea of this paper is to establish a simple approach for prediction of the ignition time of a wood particle assuming that the thermo-physical properties remain constant and ignition takes place at a characteristic ignition temperature. Using a time and space integral method, explicit

  17. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

    DEFF Research Database (Denmark)

    Larsen, Mette Voldby; Lundegaard, Claus; Lamberth, K

    2005-01-01

    Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have al.......The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php....

  18. Crack Growth-Based Predictive Methodology for the Maintenance of the Structural Integrity of Repaired and Nonrepaired Aging Engine Stationary Components

    National Research Council Canada - National Science Library

    Barron, Michael

    1999-01-01

    .... Specifically, the FAA's goal was to develop "Crack Growth-Based Predictive Methodologies for the Maintenance of the Structural Integrity of Repaired and Nonrepaired Aging Engine Stationary Components...

  19. The C. elegans Ortholog of USP7 controls DAF-16 stability in Insulin/IGF-1-like signaling.

    Science.gov (United States)

    Heimbucher, Thomas; Hunter, Tony

    2015-01-01

    FOXO family transcription factors are downstream effectors of Insulin/IGF-1 signaling (IIS) and are regulated by posttranslational modification and coregulators, including components of the ubiquitin-proteasome system (UPS). Cofactors promoting DAF-16/FOXO protein stability and function in IIS have not been described yet. In a recent study, we have identified the deubiquitylating enzyme MATH-33, the ortholog of mammalian USP7/HAUSP, as an essential DAF-16 coregulator. We found that MATH-33 actively stabilizes DAF-16 protein levels when IIS is downregulated. Here we discuss how DAF-16/FOXO transcription factors are regulated by the UPS, in particular by the interplay of E3-ubiquitin ligases and deubiquitylating enzymes, which is critical for balancing DAF-16/FOXO activity and degradation. Recent findings raise the intriguing possibility that regulated oscillations in DAF-16/FOXO steady state levels play an integral role in mechanisms controlling healthspan and lifespan extension.

  20. Integration of fluvial erosion factors for predicting landslides along meandering rivers

    Science.gov (United States)

    Chen, Yi-chin; Chang, Kang-tsung; Ho, Jui-yi

    2015-04-01

    the bank from erosion. Finally, the results also showed that the integration of fluvial erosion factors can improve the performance in predicting landsliding along meandering rivers.

  1. Sierra Nevada snowpack and runoff prediction integrating basin-wide wireless-sensor network data

    Science.gov (United States)

    Yoon, Y.; Conklin, M. H.; Bales, R. C.; Zhang, Z.; Zheng, Z.; Glaser, S. D.

    2016-12-01

    We focus on characterizing snowpack and estimating runoff from snowmelt in high elevation area (>2100 m) in Sierra Nevada for daily (for use in, e.g. flood and hydropower forecasting), seasonal (supply prediction), and decadal (long-term planning) time scale. Here, basin-wide wireless-sensor network data (ARHO, http://glaser.berkeley.edu/wsn/) is integrated into the USGS Precipitation-Runoff Modeling System (PRMS), and a case study of the American River basin is presented. In the American River basin, over 140 wireless sensors have been planted in 14 sites considering elevation gradient, slope, aspect, and vegetation density, which provides spatially distributed snow depth, temperature, solar radiation, and soil moisture from 2013. 800 m daily gridded dataset (PRISM) is used as the climate input for the PRMS. Model parameters are obtained from various sources (e.g., NLCD 2011, SSURGO, and NED) with a regionalization method and GIS analysis. We use a stepwise framework for a model calibration to improve model performance and localities of estimates. For this, entire basin is divided into 12 subbasins that include full natural flow measurements. The study period is between 1982 and 2014, which contains three major storm events and recent severe drought. Simulated snow depth and snow water equivalent (SWE) are initially compared with the water year 2014 ARHO observations. The overall results show reasonable agreements having the Nash-Sutcliffe efficiency coefficient (NS) of 0.7, ranged from 0.3 to 0.86. However, the results indicate a tendency to underestimate the SWE in a high elevation area compared with ARHO observations, which is caused by the underestimated PRISM precipitation data. Precipitation at gauge-sparse regions (e.g., high elevation area), in general, cannot be well represented in gridded datasets. Streamflow estimates of the basin outlet have NS of 0.93, percent bias of 7.8%, and normalized root mean square error of 3.6% for the monthly time scale.

  2. Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

    Science.gov (United States)

    Korvigo, Ilia; Afanasyev, Andrey; Romashchenko, Nikolay; Skoblov, Mikhail

    2018-01-01

    Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that

  3. Integrating circadian activity and gene expression profiles to predict chronotoxicity of Drosophila suzukii response to insecticides.

    Science.gov (United States)

    Hamby, Kelly A; Kwok, Rosanna S; Zalom, Frank G; Chiu, Joanna C

    2013-01-01

    Native to Southeast Asia, Drosophila suzukii (Matsumura) is a recent invader that infests intact ripe and ripening fruit, leading to significant crop losses in the U.S., Canada, and Europe. Since current D. suzukii management strategies rely heavily on insecticide usage and insecticide detoxification gene expression is under circadian regulation in the closely related Drosophila melanogaster, we set out to determine if integrative analysis of daily activity patterns and detoxification gene expression can predict chronotoxicity of D. suzukii to insecticides. Locomotor assays were performed under conditions that approximate a typical summer or winter day in Watsonville, California, where D. suzukii was first detected in North America. As expected, daily activity patterns of D. suzukii appeared quite different between 'summer' and 'winter' conditions due to differences in photoperiod and temperature. In the 'summer', D. suzukii assumed a more bimodal activity pattern, with maximum activity occurring at dawn and dusk. In the 'winter', activity was unimodal and restricted to the warmest part of the circadian cycle. Expression analysis of six detoxification genes and acute contact bioassays were performed at multiple circadian times, but only in conditions approximating Watsonville summer, the cropping season, when most insecticide applications occur. Five of the genes tested exhibited rhythmic expression, with the majority showing peak expression at dawn (ZT0, 6am). We observed significant differences in the chronotoxicity of D. suzukii towards malathion, with highest susceptibility at ZT0 (6am), corresponding to peak expression of cytochrome P450s that may be involved in bioactivation of malathion. High activity levels were not found to correlate with high insecticide susceptibility as initially hypothesized. Chronobiology and chronotoxicity of D. suzukii provide valuable insights for monitoring and control efforts, because insect activity as well as insecticide timing

  4. Integrated Predictive Tools for Customizing Microstructure and Material Properties of Additively Manufactured Aerospace Components

    Energy Technology Data Exchange (ETDEWEB)

    Radhakrishnan, Balasubramaniam [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fattebert, Jean-Luc [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Gorti, Sarma B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Haxhimali, Timor [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); El-Wardany, Tahany [United Technologies Research Center (UTRC), East Hartford, CT (United States); Acharya, Ranadip [United Technologies Research Center (UTRC), East Hartford, CT (United States); Staroselsky, Alexander [United Technologies Research Center (UTRC), East Hartford, CT (United States)

    2017-12-01

    Additive Manufacturing (AM) refers to a process by which digital three-dimensional (3-D) design data is converted to build up a component by depositing material layer-by-layer. United Technologies Corporation (UTC) is currently involved in fabrication and certification of several AM aerospace structural components made from aerospace materials. This is accomplished by using optimized process parameters determined through numerous design-of-experiments (DOE)-based studies. Certification of these components is broadly recognized as a significant challenge, with long lead times, very expensive new product development cycles and very high energy consumption. Because of these challenges, United Technologies Research Center (UTRC), together with UTC business units have been developing and validating an advanced physics-based process model. The specific goal is to develop a physics-based framework of an AM process and reliably predict fatigue properties of built-up structures as based on detailed solidification microstructures. Microstructures are predicted using process control parameters including energy source power, scan velocity, deposition pattern, and powder properties. The multi-scale multi-physics model requires solution and coupling of governing physics that will allow prediction of the thermal field and enable solution at the microstructural scale. The state-of-the-art approach to solve these problems requires a huge computational framework and this kind of resource is only available within academia and national laboratories. The project utilized the parallel phase-fields codes at Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL), along with the high-performance computing (HPC) capabilities existing at the two labs to demonstrate the simulation of multiple dendrite growth in threedimensions (3-D). The LLNL code AMPE was used to implement the UTRC phase field model that was previously developed for a model binary alloy, and

  5. [Evaluation of thermal comfort in a student population: predictive value of an integrated index (Fanger's predicted mean value].

    Science.gov (United States)

    Catenacci, G; Terzi, R; Marcaletti, G; Tringali, S

    1989-01-01

    Practical applications and predictive values of a thermal comfort index (Fanger's PRV) were verified on a sample school population (1236 subjects) by studying the relationships between thermal sensations (subjective analysis), determined by means of an individual questionnaire, and the values of thermal comfort index (objective analysis) obtained by calculating the PMV index individually in the subjects under study. In homogeneous conditions of metabolic expenditure rate and thermal impedence from clothing, significant differences were found between the two kinds of analyses. At 22 degrees C mean radiant and operative temperature, the PMV values averaged 0 and the percentage of subjects who experienced thermal comfort did not exceed 60%. The high level of subjects who were dissatisfied with their environmental thermal conditions confirms the doubts regarding the use of the PMV index as a predictive indicator of thermal comfort, especially considering that the negative answers were not homogeneous nor attributable to the small thermal fluctuations (less than 0.5 degree C) measured in the classrooms.

  6. MSOAR 2.0: Incorporating tandem duplications into ortholog assignment based on genome rearrangement

    Directory of Open Access Journals (Sweden)

    Zhang Liqing

    2010-01-01

    Full Text Available Abstract Background Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species. MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale. It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events. It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (i.e., the random duplication model. However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (i.e., the tandem duplication model. Results In this paper, we develop MSOAR 2.0, an improved system for one-to-one ortholog assignment. For a pair of input genomes, the system first focuses on the tandemly duplicated genes of each genome and tries to identify among them those that were duplicated after the speciation (i.e., the so-called inparalogs, using a simple phylogenetic tree reconciliation method. For each such set of tandemly duplicated inparalogs, all but one gene will be deleted from the concerned genome (because they cannot possibly appear in any one-to-one ortholog pairs, and MSOAR is invoked. Using both simulated and real data experiments, we show that MSOAR 2.0 is able to achieve a better sensitivity and specificity than MSOAR. In comparison with the well-known genome-scale ortholog assignment tool InParanoid, Ensembl ortholog database, and the orthology information extracted from the well-known whole-genome multiple alignment program MultiZ, MSOAR 2.0 shows the highest sensitivity. Although the specificity of MSOAR 2.0 is slightly worse than that of InParanoid in the real data experiments

  7. An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

    Directory of Open Access Journals (Sweden)

    Deborah Galpert

    2015-01-01

    Full Text Available Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

  8. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

    Science.gov (United States)

    Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

    2018-05-08

    Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on

  9. Social integration prospectively predicts changes in heart rate variability among individuals undergoing migration stress.

    Science.gov (United States)

    Gouin, Jean-Philippe; Zhou, Biru; Fitzpatrick, Stephanie

    2015-04-01

    Poor social integration increases risk for poor health. The psychobiological pathways underlying this effect are not well-understood. This study utilized a migration stress model to prospectively investigate the impact of social integration on change in high-frequency heart rate variability (HF-HRV), a marker of autonomic functioning. Sixty new international students were recruited shortly after their arrival in the host country and assessed 2 and 5 months later. At each assessment period, participants provided information on social integration and loneliness and had their resting HF-HRV evaluated. There was an overall decrease in HF-HRV over time. The magnitude of the within-person and between-person effects of social integration on HRV increased over time, such that greater social integration was associated with higher HF-HRV at later follow-ups. These results suggest that altered autonomic functioning might represent a key pathway linking social integration to health outcomes.

  10. IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

    Directory of Open Access Journals (Sweden)

    Anne-Laure Boulesteix

    2017-01-01

    Full Text Available As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper, such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

  11. Study on integrated approach of Nuclear Accident Hazard Predicting, Warning, and Optimized Controlling System based on GIS

    International Nuclear Information System (INIS)

    Tang Lijuan; Huang Shunxiang; Wang Xinming

    2012-01-01

    The issue of nuclear safety becomes the attention focus of international society after the nuclear accident happened in Fukushima. Aiming at the requirements of the prevention and controlling of Nuclear Accident establishment of Nuclear Accident Hazard Predicting, Warning and optimized Controlling System (NAPWS) is a imperative project that our country and army are desiderating, which includes multiple fields of subject as nuclear physics, atmospheric science, security science, computer science and geographical information technology, etc. Multiplatform, multi-system and multi-mode are integrated effectively based on GIS, accordingly the Predicting, Warning, and Optimized Controlling technology System of Nuclear Accident Hazard is established. (authors)

  12. The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models

    Directory of Open Access Journals (Sweden)

    Daniela Spiesová

    2014-10-01

    Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.

  13. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

    Science.gov (United States)

    Erraguntla, Madhav; Zapletal, Josef; Lawley, Mark

    2017-12-01

    The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

  14. Silencing of the Drosophila ortholog of SOX5 leads to abnormal neuronal development and behavioral impairment.

    Science.gov (United States)

    Li, Airong; Hooli, Basavaraj; Mullin, Kristina; Tate, Rebecca E; Bubnys, Adele; Kirchner, Rory; Chapman, Brad; Hofmann, Oliver; Hide, Winston; Tanzi, Rudolph E

    2017-04-15

    SOX5 encodes a transcription factor that is expressed in multiple tissues including heart, lung and brain. Mutations in SOX5 have been previously found in patients with amyotrophic lateral sclerosis (ALS) and developmental delay, intellectual disability and dysmorphic features. To characterize the neuronal role of SOX5, we silenced the Drosophila ortholog of SOX5, Sox102F, by RNAi in various neuronal subtypes in Drosophila. Silencing of Sox102F led to misorientated and disorganized michrochaetes, neurons with shorter dendritic arborization (DA) and reduced complexity, diminished larval peristaltic contractions, loss of neuromuscular junction bouton structures, impaired olfactory perception, and severe neurodegeneration in brain. Silencing of SOX5 in human SH-SY5Y neuroblastoma cells resulted in a significant repression of WNT signaling activity and altered expression of WNT-related genes. Genetic association and meta-analyses of the results in several large family-based and case-control late-onset familial Alzheimer's disease (LOAD) samples of SOX5 variants revealed several variants that show significant association with AD disease status. In addition, analysis for rare and highly penetrate functional variants revealed four novel variants/mutations in SOX5, which taken together with functional prediction analysis, suggests a strong role of SOX5 causing AD in the carrier families. Collectively, these findings indicate that SOX5 is a novel candidate gene for LOAD with an important role in neuronal function. The genetic findings warrant further studies to identify and characterize SOX5 variants that confer risk for AD, ALS and intellectual disability. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Cloning of zebrafish Mustn1 orthologs and their expression during early development.

    Science.gov (United States)

    Camarata, Troy; Vasilyev, Aleksandr; Hadjiargyrou, Michael

    2016-11-15

    Mustn1 is a small nuclear protein that is involved in the development and regeneration of the musculoskeletal system. Previous work established a role for Mustn1 in myogenic and chondrogenic differentiation. In addition, recent evidence suggests a potential role for Mustn1 in cilia function in zebrafish. A detailed study of Mustn1 expression has yet to be conducted in zebrafish. As such, we report herein the cloning of the zebrafish Mustn1 orthologs, mustn1a and mustn1b, and their expression during zebrafish embryonic and larval development. Results indicate a 44% nucleotide identity between the two paralogs. Phylogenetic analysis further confirmed that the Mustn1a and 1b predicted proteins were highly related to other vertebrate members of the Mustn1 protein family. Whole mount in situ hybridization revealed expression of both mustn1a and 1b at the 7-somite stage through 72hpf in structures such as Kupffer's vesicle, segmental mesoderm, head structures, and otic vesicle. Additionally, in 5day old larva, mustn1a and 1b expression is detected in the neurocranium, otic capsule, and the gut. Although both were expressed in the neurocranium, mustn1a was localized in the hypophyseal fenestra whereas mustn1b was found near the posterior basicapsular commissure. mustn1b also displayed expression in the ceratohyal and ceratobranchial elements of the pharyngeal skeleton. These expression patterns were verified temporally by q-PCR analysis. Taken together, we conclude that Mustn1 expression is conserved in vertebrates and that the variations in expression of the two zebrafish paralogs suggest different modes of molecular regulation. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Hierarchical predictive control scheme for distributed energy storage integrated with residential demand and photovoltaic generation

    NARCIS (Netherlands)

    Lampropoulos, I.; Garoufalis, P.; van den Bosch, P.P.J.; Kling, W.L.

    2015-01-01

    A hierarchical control scheme is defined for the energy management of a battery energy storage system which is integrated in a low-voltage distribution grid with residential customers and photovoltaic installations. The scope is the economic optimisation of the integrated system by employing

  17. Integrating three lake models into a Phytoplankton Prediction System for Lake Taihu (Taihu PPS) with Python

    NARCIS (Netherlands)

    Huang, J.; Gao, J.; Hörmann, G.; Mooij, W.M.

    2012-01-01

    In the past decade, much work has been done on integrating different lake models using general frameworks to overcome model incompatibilities. However, a framework may not be flexible enough to support applications in different fields. To overcome this problem, we used Python to integrate three lake

  18. Integration

    DEFF Research Database (Denmark)

    Emerek, Ruth

    2004-01-01

    Bidraget diskuterer de forskellige intergrationsopfattelse i Danmark - og hvad der kan forstås ved vellykket integration......Bidraget diskuterer de forskellige intergrationsopfattelse i Danmark - og hvad der kan forstås ved vellykket integration...

  19. Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

    DEFF Research Database (Denmark)

    Swanton, C.; Larkin, J.M.; Gerlinger, M.

    2010-01-01

    -cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway...

  20. Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction

    DEFF Research Database (Denmark)

    Cheng, Yongcun; Andersen, Ole Baltazar; Knudsen, Per

    2012-01-01

    The main objective of this paper is to integrate Non-Tidal Sea Level (NSL) from the joint TOPEX, Jason-1 and Jason-2 satellite altimetry with tide gauge data at the west and north coast of the United Kingdom for coastal sea level prediction. The temporal correlation coefficient between altimetric...... NSLs and tide gauge data reaches a maximum higher than 90% for each gauge. The results show that the multivariate regression approach can efficiently integrate the two types of data in the coastal waters of the area. The Multivariate Regression Model is established by integrating the along-track NSL...... from the joint TOPEX/Jason-1/Jason-2 altimeters with that from eleven tide gauges. The model results give a maximum hindcast skill of 0.95, which means maximum 95% of NSL variance can be explained by the model. The minimum Root Mean Square Error (RMSe) between altimetric observations and model...

  1. Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

    International Nuclear Information System (INIS)

    Han, Yong-Ming; Geng, Zhi-Qiang; Zhu, Qun-Xiong

    2016-01-01

    Graphical abstract: This paper proposed an energy optimization and prediction of complex petrochemical industries based on a DEA-integrated ANN approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. Meanwhile, the optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency. - Highlights: • The DEA-integrated ANN approach is proposed. • The DEA-ANN prediction model is effectively verified through the standard data source from the UCI repository. • The energy optimization and prediction framework of complex petrochemical industries based on the proposed method is obtained. • The proposed method is valid and efficient in improvement of energy efficiency in complex petrochemical plants. - Abstract: Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA

  2. A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, Hugh D.; Eisfeld, Amie J.; Sims, Amy; McDermott, Jason E.; Matzke, Melissa M.; Webb-Robertson, Bobbie-Jo M.; Tilton, Susan C.; Tchitchek, Nicholas; Josset, Laurence; Li, Chengjun; Ellis, Amy L.; Chang, Jean H.; Heegel, Robert A.; Luna, Maria L.; Schepmoes, Athena A.; Shukla, Anil K.; Metz, Thomas O.; Neumann, Gabriele; Benecke, Arndt; Smith, Richard D.; Baric, Ralph; Kawaoka, Yoshihiro; Katze, Michael G.; Waters, Katrina M.

    2013-07-25

    Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

  3. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

    Science.gov (United States)

    Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

    2012-04-01

    Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

  4. Using the Integrative Model of Behavioral Prediction to Understand College Students' STI Testing Beliefs, Intentions, and Behaviors.

    Science.gov (United States)

    Wombacher, Kevin; Dai, Minhao; Matig, Jacob J; Harrington, Nancy Grant

    2018-03-22

    To identify salient behavioral determinants related to STI testing among college students by testing a model based on the integrative model of behavioral (IMBP) prediction. 265 undergraduate students from a large university in the Southeastern US. Formative and survey research to test an IMBP-based model that explores the relationships between determinants and STI testing intention and behavior. Results of path analyses supported a model in which attitudinal beliefs predicted intention and intention predicted behavior. Normative beliefs and behavioral control beliefs were not significant in the model; however, select individual normative and control beliefs were significantly correlated with intention and behavior. Attitudinal beliefs are the strongest predictor of STI testing intention and behavior. Future efforts to increase STI testing rates should identify and target salient attitudinal beliefs.

  5. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

    Science.gov (United States)

    Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

    2015-05-27

    Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

  6. Comparison of experimental pulse-height distributions in germanium detectors with integrated-tiger-series-code predictions

    International Nuclear Information System (INIS)

    Beutler, D.E.; Halbleib, J.A.; Knott, D.P.

    1989-01-01

    This paper reports pulse-height distributions in two different types of Ge detectors measured for a variety of medium-energy x-ray bremsstrahlung spectra. These measurements have been compared to predictions using the integrated tiger series (ITS) Monte Carlo electron/photon transport code. In general, the authors find excellent agreement between experiments and predictions using no free parameters. These results demonstrate that the ITS codes can predict the combined bremsstrahlung production and energy deposition with good precision (within measurement uncertainties). The one region of disagreement observed occurs for low-energy (<50 keV) photons using low-energy bremsstrahlung spectra. In this case the ITS codes appear to underestimate the produced and/or absorbed radiation by almost an order of magnitude

  7. Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

    Directory of Open Access Journals (Sweden)

    Frank Technow

    Full Text Available Genomic selection, enabled by whole genome prediction (WGP methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E, continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC, a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

  8. OrthoVenn: a web server for genome wide comparison and annotation of orthologous clusters across multiple species

    Science.gov (United States)

    Genome wide analysis of orthologous clusters is an important component of comparative genomics studies. Identifying the overlap among orthologous clusters can enable us to elucidate the function and evolution of proteins across multiple species. Here, we report a web platform named OrthoVenn that i...

  9. Cloning and transcription analysis of an AGAMOUS- and SEEDSTICK ortholog in the orchid Dendrobium thyrsiflorum (Reichb. f.)

    DEFF Research Database (Denmark)

    Skipper, Martin; Johansen, Louise Buchholt; Pedersen, Kim B.

    2006-01-01

    Studies have shown that several plant species posses AGAMOUS (AG) and SEEDSTICK (STK) orthologs. These genes are part of the so-called C- and D MADS-box gene lineages and play key roles in ovule development in Arabidopsis thaliana. We have cloned an AG- and STK ortholog in the orchid Dendrobium...

  10. Predicting Tropical Cyclone Destructive Potential by Integrated Kinetic Energy According to the Powell/Reinhold Scale

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A method of predicting the destructive capacity of a tropical cyclone based on a new Wind Destructive Potential (WDP) and Storm Surge Destructive Potential (SDP)...

  11. Numerical Weather Prediction and Relative Economic Value framework to improve Integrated Urban Drainage- Wastewater management

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas

    domains during which the IUDWS can be coupled with the electrical smart grid to optimise its energy consumption. The REV framework was used to determine which decision threshold of the EPS (i.e. number of ensemble members predicting an event) provides the highest benefit for a given situation...... in cities where space is scarce and large-scale construction work a nuisance. This the-sis focuses on flow domain predictions of IUDWS from numerical weather prediction (NWP) to select relevant control objectives for the IUDWS and develops a framework based on the relative economic value (REV) approach...... to evaluate when acting on the forecast is beneficial or not. Rainfall forecasts are extremely valuable for estimating near future storm-water-related impacts on the IUDWS. Therefore, weather radar extrapolation “nowcasts” provide valuable predictions for RTC. However, radar nowcasts are limited...

  12. Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2017-01-01

    Full Text Available This study investigates predicting the pullout capacity of small ground anchors using nonlinear computing techniques. The input-output prediction model for the nonlinear Hammerstein-Wiener (NHW and delay inputs for the adaptive neurofuzzy inference system (DANFIS are developed and utilized to predict the pullout capacity. The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study. The in situ data collection and statistical performances are used to evaluate the models performance. Results show that the developed models enhance the precision of predicting the pullout capacity when compared with previous studies. Also, the DANFIS model performance is proven to be better than other models used to detect the pullout capacity of ground anchors.

  13. Brain mechanisms in religion and spirituality: An integrative predictive processing framework.

    Science.gov (United States)

    van Elk, Michiel; Aleman, André

    2017-02-01

    We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated with religious visions and ecstatic experiences; multisensory brain areas and the default mode network are involved in self-transcendent experiences; the Theory of Mind-network is associated with prayer experiences and over attribution of intentionality; top-down mechanisms instantiated in the anterior cingulate cortex and the medial prefrontal cortex could be involved in acquiring and maintaining intuitive supernatural beliefs. We compare the predictive processing model with two-systems accounts of religion and spirituality, by highlighting the central role of prediction error monitoring. We conclude by presenting novel predictions for future research and by discussing the philosophical and theological implications of neuroscientific research on religion and spirituality. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    Directory of Open Access Journals (Sweden)

    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  15. Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

    Directory of Open Access Journals (Sweden)

    Bartek Wilczynski

    Full Text Available Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal

  16. The Nature and Predictive Value of Mothers’ Beliefs Regarding Infants’ and Toddlers’ TV/Video Viewing: Applying the Integrative Model of Behavioral Prediction

    Science.gov (United States)

    Vaala, Sarah E.

    2014-01-01

    Viewing television and video programming has become a normative behavior among US infants and toddlers. Little is understood about parents’ decision-making about the extent of their young children’s viewing, though numerous organizations are interested in reducing time spent viewing among infants and toddlers. Prior research has examined parents’ belief in the educational value of TV/videos for young children and the predictive value of this belief for understanding infant/toddler viewing rates, though other possible salient beliefs remain largely unexplored. This study employs the integrative model of behavioral prediction (Fishbein & Ajzen, 2010) to examine 30 maternal beliefs about infants’ and toddlers’ TV/video viewing which were elicited from a prior sample of mothers. Results indicate that mothers tend to hold more positive than negative beliefs about the outcomes associated with young children’s TV/video viewing, and that the nature of the aggregate set of beliefs is predictive of their general attitudes and intentions to allow their children to view, as well as children’s estimated viewing rates. Analyses also uncover multiple dimensions within the full set of beliefs, which explain more variance in mothers’ attitudes and intentions and children’s viewing than the uni-dimensional index. The theoretical and practical implications of the findings are discussed. PMID:25431537

  17. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  18. An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

    Science.gov (United States)

    Liu, Tao; Chen, Lei; Pan, Xiaoyong

    2018-05-31

    Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Cross activity of orthologous WRKY transcription factors in wheat and Arabidopsis

    NARCIS (Netherlands)

    Poietti, S.; Bertini, L.; Ent, S. van der; Leon Reyes, H.A.; Pieterse, C.M.J.; Tucci, M.; Caporale, C.; Caruso, C.

    2011-01-01

    WRKY proteins are transcription factors involved in many plant processes including plant responses to pathogens. Here, the cross activity of TaWRKY78 from the monocot wheat and AtWRKY20 from the dicot Arabidopsis on the cognate promoters of the orthologous PR4-type genes wPR4e and AtHEL of wheat and

  20. Synteny of orthologous genes conserved in human, mouse, snake, Drosophila, nematode, and fission yeast

    Czech Academy of Sciences Publication Activity Database

    Trachtulec, Zdeněk; Forejt, Jiří

    2001-01-01

    Roč. 12, č. 3 (2001), s. 227-231 ISSN 0938-8990 Institutional research plan: CEZ:AV0Z5052915 Keywords : synteny of orthologous genes Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.318, year: 2001

  1. Database Description - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available e relevant data in the databases. By submitting queries to the PGDBj Ortholog DB with keywords or amino acid sequences, users... taxa including both model plants and crop plants. Following the links obtained, users can retrieve the actu

  2. Proteinortho: detection of (co-)orthologs in large-scale analysis.

    Science.gov (United States)

    Lechner, Marcus; Findeiss, Sven; Steiner, Lydia; Marz, Manja; Stadler, Peter F; Prohaska, Sonja J

    2011-04-28

    Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.

  3. Proteinortho: Detection of (Co-orthologs in large-scale analysis

    Directory of Open Access Journals (Sweden)

    Steiner Lydia

    2011-04-01

    Full Text Available Abstract Background Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. Results The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. Conclusions Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.

  4. Integration of Multiple Data Sources for predicting the Engagement of Students in Practical Activities

    Directory of Open Access Journals (Sweden)

    Llanos Tobarra

    2014-09-01

    Full Text Available This work presents the integration of an automatic assessment system for virtual/remote laboratories and the institutional Learning Management System (LMS, in order to analyze the students’ progress and their collaborative learning in virtual/remote laboratories. As a result of this integration, it is feasible to extract useful information for the characterization of the students’ learning process and detecting the students’ engagement with the practical activities of our subjects. From this integration, a dashboard has been created to graphically present to lecturers the analyzed results. Thanks to this, faculty can use the analyzed information in order to guide the learning/teaching process of each student. As an example, a subject focused on the configuration of network services has been chosen to implement our proposal.

  5. Assessing the evolutionary rate of positional orthologous genes in prokaryotes using synteny data

    Directory of Open Access Journals (Sweden)

    Lespinet Olivier

    2007-11-01

    Full Text Available Abstract Background Comparison of completely sequenced microbial genomes has revealed how fluid these genomes are. Detecting synteny blocks requires reliable methods to determining the orthologs among the whole set of homologs detected by exhaustive comparisons between each pair of completely sequenced genomes. This is a complex and difficult problem in the field of comparative genomics but will help to better understand the way prokaryotic genomes are evolving. Results We have developed a suite of programs that automate three essential steps to study conservation of gene order, and validated them with a set of 107 bacteria and archaea that cover the majority of the prokaryotic taxonomic space. We identified the whole set of shared homologs between two or more species and computed the evolutionary distance separating each pair of homologs. We applied two strategies to extract from the set of homologs a collection of valid orthologs shared by at least two genomes. The first computes the Reciprocal Smallest Distance (RSD using the PAM distances separating pairs of homologs. The second method groups homologs in families and reconstructs each family's evolutionary tree, distinguishing bona fide orthologs as well as paralogs created after the last speciation event. Although the phylogenetic tree method often succeeds where RSD fails, the reverse could occasionally be true. Accordingly, we used the data obtained with either methods or their intersection to number the orthologs that are adjacent in for each pair of genomes, the Positional Orthologous Genes (POGs, and to further study their properties. Once all these synteny blocks have been detected, we showed that POGs are subject to more evolutionary constraints than orthologs outside synteny groups, whichever the taxonomic distance separating the compared organisms. Conclusion The suite of programs described in this paper allows a reliable detection of orthologs and is useful for evaluating gene

  6. More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions

    Science.gov (United States)

    Vinck, Martin; Bosman, Conrado A.

    2016-01-01

    During visual stimulation, neurons in visual cortex often exhibit rhythmic and synchronous firing in the gamma-frequency (30–90 Hz) band. Whether this phenomenon plays a functional role during visual processing is not fully clear and remains heavily debated. In this article, we explore the function of gamma-synchronization in the context of predictive and efficient coding theories. These theories hold that sensory neurons utilize the statistical regularities in the natural world in order to improve the efficiency of the neural code, and to optimize the inference of the stimulus causes of the sensory data. In visual cortex, this relies on the integration of classical receptive field (CRF) data with predictions from the surround. Here we outline two main hypotheses about gamma-synchronization in visual cortex. First, we hypothesize that the precision of gamma-synchronization reflects the extent to which CRF data can be accurately predicted by the surround. Second, we hypothesize that different cortical columns synchronize to the extent that they accurately predict each other’s CRF visual input. We argue that these two hypotheses can account for a large number of empirical observations made on the stimulus dependencies of gamma-synchronization. Furthermore, we show that they are consistent with the known laminar dependencies of gamma-synchronization and the spatial profile of intercolumnar gamma-synchronization, as well as the dependence of gamma-synchronization on experience and development. Based on our two main hypotheses, we outline two additional hypotheses. First, we hypothesize that the precision of gamma-synchronization shows, in general, a negative dependence on RF size. In support, we review evidence showing that gamma-synchronization decreases in strength along the visual hierarchy, and tends to be more prominent in species with small V1 RFs. Second, we hypothesize that gamma-synchronized network dynamics facilitate the emergence of spiking output that

  7. Reliability of nine programs of topological predictions and their application to integral membrane channel and carrier proteins.

    Science.gov (United States)

    Reddy, Abhinay; Cho, Jaehoon; Ling, Sam; Reddy, Vamsee; Shlykov, Maksim; Saier, Milton H

    2014-01-01

    We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K(+) transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account. © 2014 S. Karger AG, Basel.

  8. Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia.

    Science.gov (United States)

    Kelly, Rachel S; Croteau-Chonka, Damien C; Dahlin, Amber; Mirzakhani, Hooman; Wu, Ann C; Wan, Emily S; McGeachie, Michael J; Qiu, Weiliang; Sordillo, Joanne E; Al-Garawi, Amal; Gray, Kathryn J; McElrath, Thomas F; Carey, Vincent J; Clish, Clary B; Litonjua, Augusto A; Weiss, Scott T; Lasky-Su, Jessica A

    2017-01-01

    Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive. This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms. Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods. In total, 72 (0.9%) metabolite features were associated (pIntegration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system. These findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.

  9. Predictions of structural integrity of steam generator tubes under normal operating, accident, and severe accident conditions

    International Nuclear Information System (INIS)

    Majumdar, S.

    1996-09-01

    Available models for predicting failure of flawed and unflawed steam generator tubes under normal operating, accident, and severe accident conditions are reviewed. Tests conducted in the past, though limited, tended to show that the earlier flow-stress model for part-through-wall axial cracks overestimated the damaging influence of deep cracks. This observation is confirmed by further tests at high temperatures as well as by finite element analysis. A modified correlation for deep cracks can correct this shortcoming of the model. Recent tests have shown that lateral restraint can significantly increase the failure pressure of tubes with unsymmetrical circumferential cracks. This observation is confirmed by finite element analysis. The rate-independent flow stress models that are successful at low temperatures cannot predict the rate sensitive failure behavior of steam generator tubes at high temperatures. Therefore, a creep rupture model for predicting failure is developed and validated by tests under varying temperature and pressure loading expected during severe accidents

  10. Integrating Self-Determination and Job Demands-Resources Theory in Predicting Mental Health Provider Burnout.

    Science.gov (United States)

    Dreison, Kimberly C; White, Dominique A; Bauer, Sarah M; Salyers, Michelle P; McGuire, Alan B

    2018-01-01

    Limited progress has been made in reducing burnout in mental health professionals. Accordingly, we identified factors that might protect against burnout and could be productive focal areas for future interventions. Guided by self-determination theory, we examined whether supervisor autonomy support, self-efficacy, and staff cohesion predict provider burnout. 358 staff from 13 agencies completed surveys. Higher levels of supervisor autonomy support, self-efficacy, and staff cohesion were predictive of lower burnout, even after accounting for job demands. Although administrators may be limited in their ability to reduce job demands, our findings suggest that increasing core job resources may be a viable alternative.

  11. Evaluating empirical/analytical techniques to predict structural integrity of pipe containing surface flaws

    International Nuclear Information System (INIS)

    Reuter, W.G.; Server, W.L.

    1982-01-01

    Data from flat-plate specimens containing either triangular-, ellipsoidal- or rectangular-shaped surface flaws were evaluated by several potential analytical techniques. These techniques were modified as needed to predict conditions for initiation of subcritical crack growth, for the defect to penetrate the 6.4 mm (0.25 in.) wall thickness, and for instability (plastic or unstable). The modified analytical techniques developed from the plate specimens were then used to make predictions which are compared with test results obtained from pipe specimens containing triangular-shaped surface flaws

  12. Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

    OpenAIRE

    Namvar, Anahita; Naderpour, Mohsen

    2018-01-01

    As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be o...

  13. Improved prediction of reservoir behavior through integration of quantitative geological and petrophysical data

    Energy Technology Data Exchange (ETDEWEB)

    Auman, J. B.; Davies, D. K.; Vessell, R. K.

    1997-08-01

    Methodology that promises improved reservoir characterization and prediction of permeability, production and injection behavior during primary and enhanced recovery operations was demonstrated. The method is based on identifying intervals of unique pore geometry by a combination of image analysis techniques and traditional petrophysical measurements to calculate rock type and estimate permeability and saturation. Results from a complex carbonate and sandstone reservoir were presented as illustrative examples of the versatility and high level of accuracy of this method in predicting reservoir quality. 16 refs., 5 tabs., 14 figs.

  14. Predicting older adults' maintenance in exercise participation using an integrated social psychological model

    NARCIS (Netherlands)

    Stiggelbout, M.; Hopman-Rock, M.; Crone, M.; Lechner, L.; Mechelen, W. van

    2006-01-01

    Little is known about the predictors of maintenance in organized exercise programmes. The aim of this study was to investigate the behavioral predictors of maintenance of exercise participation in older adults, using an integrated social psychological model. To this end, we carried out a prospective

  15. Attachment Theory and Theory of Planned Behavior: An Integrative Model Predicting Underage Drinking

    Science.gov (United States)

    Lac, Andrew; Crano, William D.; Berger, Dale E.; Alvaro, Eusebio M.

    2013-01-01

    Research indicates that peer and maternal bonds play important but sometimes contrasting roles in the outcomes of children. Less is known about attachment bonds to these 2 reference groups in young adults. Using a sample of 351 participants (18 to 20 years of age), the research integrated two theoretical traditions: attachment theory and theory of…

  16. Prediction of ionizing radiation effects in integrated circuits using black-box models

    International Nuclear Information System (INIS)

    Williamson, P.W.

    1976-10-01

    A method is described which allows general black-box modelling of integrated circuits as distinct from the existing method of deriving the radiation induced response of the model from actual terminal measurements on the device during irradiation. Both digital and linear circuits are discussed. (author)

  17. Brain mechanisms in religion and spirituality : An integrative predictive processing framework

    NARCIS (Netherlands)

    van Elk, Michiel; Aleman, Andre

    We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated

  18. Analysis and prediction of daily physical activity level data using autoregressive integrated moving average models

    NARCIS (Netherlands)

    Long, Xi; Pauws, S.C.; Pijl, M.; Lacroix, J.; Goris, A.H.C.; Aarts, R.M.

    2009-01-01

    Results are provided on predicting daily physical activity level (PAL) data from past data of participants of a physical activity lifestyle program aimed at promoting a healthier lifestyle consisting of more physical exercise. The PAL data quantifies the level of a person’s daily physical activity

  19. Integrating predictive information into an agro-economic model to guide agricultural management

    Science.gov (United States)

    Zhang, Y.; Block, P.

    2016-12-01

    Skillful season-ahead climate predictions linked with responsive agricultural planning and management have the potential to reduce losses, if adopted by farmers, particularly for rainfed-dominated agriculture such as in Ethiopia. Precipitation predictions during the growing season in major agricultural regions of Ethiopia are used to generate predicted climate yield factors, which reflect the influence of precipitation amounts on crop yields and serve as inputs into an agro-economic model. The adapted model, originally developed by the International Food Policy Research Institute, produces outputs of economic indices (GDP, poverty rates, etc.) at zonal and national levels. Forecast-based approaches, in which farmers' actions are in response to forecasted conditions, are compared with no-forecast approaches in which farmers follow business as usual practices, expecting "average" climate conditions. The effects of farmer adoption rates, including the potential for reduced uptake due to poor predictions, and increasing forecast lead-time on economic outputs are also explored. Preliminary results indicate superior gains under forecast-based approaches.

  20. Data assimilation in optimizing and integrating soil and water quality water model predictions at different scales

    Science.gov (United States)

    Relevant data about subsurface water flow and solute transport at relatively large scales that are of interest to the public are inherently laborious and in most cases simply impossible to obtain. Upscaling in which fine-scale models and data are used to predict changes at the coarser scales is the...

  1. An Other Perspective on Personality: Meta-Analytic Integration of Observers' Accuracy and Predictive Validity

    Science.gov (United States)

    Connelly, Brian S.; Ones, Deniz S.

    2010-01-01

    The bulk of personality research has been built from self-report measures of personality. However, collecting personality ratings from other-raters, such as family, friends, and even strangers, is a dramatically underutilized method that allows better explanation and prediction of personality's role in many domains of psychology. Drawing…

  2. Integration of meanline and one-dimensional methods for prediction of pulsating performance of a turbocharger turbine

    International Nuclear Information System (INIS)

    Chiong, M.S.; Rajoo, S.; Romagnoli, A.; Costall, A.W.; Martinez-Botas, R.F.

    2014-01-01

    Highlights: • Unsteady turbine performance prediction by integrating the 1-D and meanline models. • The optimum discretization length/diameter ratio is identified. • No improvement is gained by increasing the number of rotor entries. • The predicted instantaneous mass flow and output power are analysed in detail. - Abstract: Stringent emission regulations are driving engine manufacturers to increase investment into enabling technologies to achieve better specific fuel consumption, thermal efficiency and most importantly carbon reduction. Engine downsizing is seen as a key enabler to successfully achieve all of these requirements. Boosting through turbocharging is widely regarded as one of the most promising technologies for engine downsizing. However, the wide range of engine speeds and loads requires enhanced quality of engine-turbocharger matching, compared to the conventional approach which considers only the full load condition. Thus, development of computational models capable of predicting the unsteady behaviour of a turbocharger turbine is crucial to the overall matching process. A purely one-dimensional (1D) turbine model is capable of good unsteady swallowing capacity predictions, however it has not been fully exploited to predict instantaneous turbine power. On the contrary, meanline models (zero-dimensional) are regarded as a good tool to determine turbine efficiency in steady state but they do not include any information about the pressure wave action occurring within the turbine. This paper explores an alternative methodology to predict instantaneous turbine power and swallowing capacity by integrating one-dimensional and meanline models. A single entry mixed-flow turbine is modelled using a 1D gas dynamic code to solve the unsteady flow state in the volute, consequently used as the input for a meanline model to evaluate the instantaneous turbine power. The key in the effectiveness of this methodology relies on the synchronisation of the flow

  3. Improving sexual health communication between older women and their providers: how the integrative model of behavioral prediction can help.

    Science.gov (United States)

    Hughes, Anne K; Rostant, Ola S; Curran, Paul G

    2014-07-01

    Talking about sexual health can be a challenge for some older women. This project was initiated to identify key factors that improve communication between aging women and their primary care providers. A sample of women (aged 60+) completed an online survey regarding their intent to communicate with a provider about sexual health. Using the integrative model of behavioral prediction as a guide, the survey instrument captured data on attitudes, perceived norms, self-efficacy, and intent to communicate with a provider about sexual health. Data were analyzed using structural equation modeling. Self-efficacy and perceived norms were the most important factors predicting intent to communicate for this sample of women. Intent did not vary with race, but mean scores of the predictors of intent varied for African American and White women. Results can guide practice and intervention with ethnically diverse older women who may be struggling to communicate about their sexual health concerns. © The Author(s) 2013.

  4. Experimental validation of alternate integral-formulation method for predicting acoustic radiation based on particle velocity measurements.

    Science.gov (United States)

    Ni, Zhi; Wu, Sean F

    2010-09-01

    This paper presents experimental validation of an alternate integral-formulation method (AIM) for predicting acoustic radiation from an arbitrary structure based on the particle velocities specified on a hypothetical surface enclosing the target source. Both the normal and tangential components of the particle velocity on this hypothetical surface are measured and taken as the input to AIM codes to predict the acoustic pressures in both exterior and interior regions. The results obtained are compared with the benchmark values measured by microphones at the same locations. To gain some insight into practical applications of AIM, laser Doppler anemometer (LDA) and double hotwire sensor (DHS) are used as measurement devices to collect the particle velocities in the air. Measurement limitations of using LDA and DHS are discussed.

  5. An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.

    Science.gov (United States)

    Ferlaino, Michael; Rogers, Mark F; Shihab, Hashem A; Mort, Matthew; Cooper, David N; Gaunt, Tom R; Campbell, Colin

    2017-10-06

    Small insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome. We present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk. FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.

  6. Internally-directed cognition and mindfulness:An integrative perspective derived from reactive versus predictive control systems theory

    Directory of Open Access Journals (Sweden)

    Mattie eTops

    2014-05-01

    Full Text Available In the present paper we will apply the Predictive And Reactive Control Systems (PARCS theory as a framework that integrates competing theories of neural substrates of awareness by describing the default mode network (DMN and anterior insula (AI as parts of two different behavioral and homeostatic control systems. The DMN, a network that becomes active at rest when there is no external stimulation or task to perform, has been implicated in self-reflective awareness and prospection. By contrast, the AI is associated with awareness and task-related attention. This has led to competing theories stressing the role of the DMN in self-awareness versus the role of interoceptive and emotional information integration in the AI in awareness of the emotional moment. In PARCS, the respective functions of the DMN and AI in a specific control system explains their association with different qualities of awareness, and how mental states can shift from one state (e.g., prospective self-reflection to the other (e.g., awareness of the emotional moment depending on the relative dominance of control systems. These shifts between reactive and predictive control are part of processes that enable the intake of novel information, integration of this novel information within existing knowledge structures, and the creation of a continuous personal context in which novel information can be integrated and understood. As such, PARCS can explain key characteristics of mental states, such as their temporal and spatial focus (e.g., a focus on the here and now vs. the future; a 1st person vs. a 3rd person perspective. PARCS further relates mental states to brain states and functions, such as activation of the DMN or hemispheric asymmetry in frontal cortical functions. Together, PARCS deepens the understanding of a broad range of mental states, including mindfulness, mind wandering, rumination, autobiographical memory, imagery, and the experience of self.

  7. Wetland habitat disturbance best predicts metrics of an amphibian index of biotic integrity

    Science.gov (United States)

    Stapanian, Martin A.; Micacchion, Mick; Adams, Jean V.

    2015-01-01

    Regression and classification trees were used to identify the best predictors of the five component metrics of the Ohio Amphibian Index of Biotic Integrity (AmphIBI) in 54 wetlands in Ohio, USA. Of the 17 wetland- and surrounding landscape-scale variables considered, the best predictor for all AmphIBI metrics was habitat alteration and development within the wetland. The results were qualitatively similar to the best predictors for a wetland vegetation index of biotic integrity, suggesting that similar management practices (e.g., reducing or eliminating nutrient enrichment from agriculture, mowing, grazing, logging, and removing down woody debris) within the boundaries of the wetland can be applied to effectively increase the quality of wetland vegetation and amphibian communities.

  8. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  9. Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots

    Science.gov (United States)

    2014-01-01

    Background Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss. Results This work proposes a new method, βACV ASA , to predict the change of binding free energy after alanine mutations. βACV ASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector (ACV). A β contact between two atoms is a direct contact without being interrupted by any other atom between them. A β contact’s potential contribution to protein binding is also supposed to be inversely proportional to its ASA to follow the water exclusion hypothesis of binding hot spots. Tested on a dataset of 396 alanine mutations, our method is found to be superior in classification performance to many other methods, including Robetta, FoldX, HotPOINT, an ACV method of β contacts without ASA integration, and ACV ASA methods (similar to βACV ASA but based on distance-cutoff contacts). Based on our data analysis and results, we can draw conclusions that: (i) our method is powerful in the prediction of binding free energy change after alanine mutation; (ii) β contacts are better than distance-cutoff contacts for modeling the well-organized protein-binding interfaces; (iii) β contacts usually are only a small fraction number of the distance-based contacts; and (iv) water exclusion is a necessary condition for a residue to become a binding hot spot. Conclusions βACV ASA is designed using the advantages of both β contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation. PMID:24568581

  10. Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

    Science.gov (United States)

    Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

    2015-12-01

    Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we

  11. Integrated approach for sinkhole evaluation and evolution prediction in the Central Ebro Basin (NE Spain

    Directory of Open Access Journals (Sweden)

    Oscar Pueyo Anchuela

    2017-06-01

    Full Text Available Evaluation of karst hazards benefits from the integration of different techniques, methodologies and approaches. Each one presents a different signature and is sensitive to certain indicators related to karst hazards. In some cases, detailed analysis permits the evaluation of representativeness either from isolated approaches or by means of integrated analyses. In this study, we present the evaluation of an area with high density of karstic collapses at different evolutionary stages through the integration of surficial, historical, geomorphological and geophysical data in order to finally define the evolutionary model for karst activity development. The obtained dataset permits to identify different steps in sinkhole evolution: (i cavities and open sinkholes, (ii filling of these cavities, with materials having different signatures, (iii the progression from collapses to subsidence sinkholes and (iv enlargement through collapses in marginal areas of previous sinkholes. The presence of different stages of this evolutionary model permits to determine their own signatures that can be of application in contexts where analysis cannot be so systematic and also to evaluate the definition of the marginal areas of previous sinkholes as the most hazardous sectors.

  12. Adherence predicts symptomatic and psychosocial remission in schizophrenia: Naturalistic study of patient integration in the community.

    Science.gov (United States)

    Bernardo, Miguel; Cañas, Fernando; Herrera, Berta; García Dorado, Marta

    Psychosocial functioning in patients with schizophrenia attended in daily practice is an understudied aspect. The aim of this study was to assess the relationship between symptomatic and psychosocial remission and adherence to treatment in schizophrenia. This cross-sectional, non-interventional, and multicenter study assessed symptomatic and psychosocial remission and community integration of 1,787 outpatients with schizophrenia attended in Spanish mental health services. Adherence to antipsychotic medication in the previous year was categorized as≥80% vs.<80%. Symptomatic remission was achieved in 28.5% of patients, and psychosocial remission in 26.1%. A total of 60.5% of patients were classified as adherent to antipsychotic treatment and 41% as adherent to non-pharmacological treatment. During the index visit, treatment was changed in 28.4% of patients, in 31.1% of them because of low adherence (8.8% of the total population). Adherent patients showed higher percentages of symptomatic and psychosocial remission than non-adherent patients (30.5 vs. 25.4%, P<.05; and 32 vs. 17%, P<.001, respectively). Only 3.5% of the patients showed an adequate level of community integration, which was also higher among adherent patients (73.0 vs. 60.1%, P<.05). Adherence to antipsychotic medication was associated with symptomatic and psychosocial remission as well as with community integration. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

  13. A novel approach for predicting the response of the spectrometer for INTEGRAL satellite

    International Nuclear Information System (INIS)

    Kshetri, R.

    2013-01-01

    A basic phenomenological approach has been presented in three recent papers (Kshetri R., 2012. JINST 7, P04008; Kshetri R., 2012. JINST 7, P07006; Kshetri R., 2012. JINST 7, P12007) for understanding the operation of encapsulated type composite detectors including the SPI spectrometer. In the present paper, we have considered the fact that the experimental two-fold events between two detectors include the three and higher fold events between the same two detectors. The formalism has been further developed and the peak-to-total ratio of a general composite detector are predicted for energy region with no direct experimental information about them. At 8 MeV, the peak-to-total ratio for the SPI spectrometer and a very large detector (comprising of infinite number of single HPGe modules) are found to be 9% and 12%, respectively. The predictions for fold distribution of the SPI spectrometer are found to be in agreement with experimental data. Our formulation does not include ad-hoc fits, but expressions that are justifiable by probability flow arguments. Instead of using an empirical method or simulation, we present a novel approach for calculating the peak-to-total ratio of the SPI spectrometer for high gamma energies. - Highlights: ► Operation of SPI is described in terms of few probability amplitudes and a parameter. ► Predictions for peak-to-total ratio are given for inaccessible energy region. ► Predictions for fold distribution agree with experimental data up to 8 MeV. ► This paper is the sixth in the series of papers on composite germanium detectors

  14. Experimental Validation of Energy Resources Integration in Microgrids via Distributed Predictive Control

    DEFF Research Database (Denmark)

    Mantovani, Giancarlo; Costanzo, Giuseppe Tommaso; Marinelli, Mattia

    2014-01-01

    This paper presents an innovative control scheme for the management of energy consumption in commercial build- ings with local energy production, such as photovoltaic panels or wind turbine and an energy storage unit. The presented scheme is based on distributed model predictive controllers, which...... sources, a vanadium redox battery system, resistive load, and a point of common coupling to the national grid. Several experiments are carried to assess the performance of the control scheme in managing local energy pro- duction and consumption....

  15. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue

    OpenAIRE

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...

  16. Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin.

    Science.gov (United States)

    Rathore, Anurag S; Mittal, Shachi; Lute, Scott; Brorson, Kurt

    2012-01-01

    Separation media, in particular chromatography media, is typically one of the major contributors to the cost of goods for production of a biotechnology therapeutic. To be cost-effective, it is industry practice that media be reused over several cycles before being discarded. The traditional approach for estimating the number of cycles a particular media can be reused for involves performing laboratory scale experiments that monitor column performance and carryover. This dataset is then used to predict the number of cycles the media can be used at manufacturing scale (concurrent validation). Although, well accepted and widely practiced, there are challenges associated with extrapolating the laboratory scale data to manufacturing scale due to differences that may exist across scales. Factors that may be different include: level of impurities in the feed material, lot to lot variability in feedstock impurities, design of the column housing unit with respect to cleanability, and homogeneity of the column packing. In view of these challenges, there is a need for approaches that may be able to predict column underperformance at the manufacturing scale over the product lifecycle. In case such an underperformance is predicted, the operators can unpack and repack the chromatography column beforehand and thus avoid batch loss. Chemometrics offers one such solution. In this article, we present an application of chemometrics toward the analysis of a set of chromatography profiles with the intention of predicting the various events of column underperformance including the backpressure buildup and inefficient deoxyribonucleic acid clearance. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

  17. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

    Science.gov (United States)

    Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

    2016-09-01

    We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  18. Integrating piecewise linear representation and ensemble neural network for stock price prediction

    OpenAIRE

    Asaduzzaman, Md.; Shahjahan, Md.; Ahmed, Fatema Johera; Islam, Md. Monirul; Murase, Kazuyuki

    2014-01-01

    Stock Prices are considered to be very dynamic and susceptible to quick changes because of the underlying nature of the financial domain, and in part because of the interchange between known parameters and unknown factors. Of late, several researchers have used Piecewise Linear Representation (PLR) to predict the stock market pricing. However, some improvements are needed to avoid the appropriate threshold of the trading decision, choosing the input index as well as improving the overall perf...

  19. MBGD update 2015: microbial genome database for flexible ortholog analysis utilizing a diverse set of genomic data.

    Science.gov (United States)

    Uchiyama, Ikuo; Mihara, Motohiro; Nishide, Hiroyo; Chiba, Hirokazu

    2015-01-01

    The microbial genome database for comparative analysis (MBGD) (available at http://mbgd.genome.ad.jp/) is a comprehensive ortholog database for flexible comparative analysis of microbial genomes, where the users are allowed to create an ortholog table among any specified set of organisms. Because of the rapid increase in microbial genome data owing to the next-generation sequencing technology, it becomes increasingly challenging to maintain high-quality orthology relationships while allowing the users to incorporate the latest genomic data available into an analysis. Because many of the recently accumulating genomic data are draft genome sequences for which some complete genome sequences of the same or closely related species are available, MBGD now stores draft genome data and allows the users to incorporate them into a user-specific ortholog database using the MyMBGD functionality. In this function, draft genome data are incorporated into an existing ortholog table created only from the complete genome data in an incremental manner to prevent low-quality draft data from affecting clustering results. In addition, to provide high-quality orthology relationships, the standard ortholog table containing all the representative genomes, which is first created by the rapid classification program DomClust, is now refined using DomRefine, a recently developed program for improving domain-level clustering using multiple sequence alignment information. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

    Science.gov (United States)

    Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

    2016-01-01

    We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

    Science.gov (United States)

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  2. Integration of Pathway Knowledge and Dynamic Bayesian Networks for the Prediction of Oral Cancer Recurrence.

    Science.gov (United States)

    Kourou, Konstantina; Papaloukas, Costas; Fotiadis, Dimitrios I

    2017-03-01

    Oral squamous cell carcinoma has been characterized as a complex disease which involves dynamic genomic changes at the molecular level. These changes indicate the worth to explore the interactions of the molecules and especially of differentially expressed genes that contribute to cancer progression. Moreover, based on this knowledge the identification of differentially expressed genes and related molecular pathways is of great importance. In the present study, we exploit differentially expressed genes in order to further perform pathway enrichment analysis. According to our results we found significant pathways in which the disease associated genes have been identified as strongly enriched. Furthermore, based on the results of the pathway enrichment analysis we propose a methodology for predicting oral cancer recurrence using dynamic Bayesian networks. The methodology takes into consideration time series gene expression data in order to predict a disease recurrence. Subsequently, we are able to conjecture about the causal interactions between genes in consecutive time intervals. Concerning the performance of the predictive models, the overall accuracy of the algorithm is 81.8% and the area under the ROC curve 89.2% regarding the knowledge from the overrepresented pre-NOTCH Expression and processing pathway.

  3. Clusters of orthologous genes for 41 archaeal genomes and implications for evolutionary genomics of archaea

    Directory of Open Access Journals (Sweden)

    Wolf Yuri I

    2007-11-01

    Full Text Available Abstract Background An evolutionary classification of genes from sequenced genomes that distinguishes between orthologs and paralogs is indispensable for genome annotation and evolutionary reconstruction. Shortly after multiple genome sequences of bacteria, archaea, and unicellular eukaryotes became available, an attempt on such a classification was implemented in Clusters of Orthologous Groups of proteins (COGs. Rapid accumulation of genome sequences creates opportunities for refining COGs but also represents a challenge because of error amplification. One of the practical strategies involves construction of refined COGs for phylogenetically compact subsets of genomes. Results New Archaeal Clusters of Orthologous Genes (arCOGs were constructed for 41 archaeal genomes (13 Crenarchaeota, 27 Euryarchaeota and one Nanoarchaeon using an improved procedure that employs a similarity tree between smaller, group-specific clusters, semi-automatically partitions orthology domains in multidomain proteins, and uses profile searches for identification of remote orthologs. The annotation of arCOGs is a consensus between three assignments based on the COGs, the CDD database, and the annotations of homologs in the NR database. The 7538 arCOGs, on average, cover ~88% of the genes in a genome compared to a ~76% coverage in COGs. The finer granularity of ortholog identification in the arCOGs is apparent from the fact that 4538 arCOGs correspond to 2362 COGs; ~40% of the arCOGs are new. The archaeal gene core (protein-coding genes found in all 41 genome consists of 166 arCOGs. The arCOGs were used to reconstruct gene loss and gene gain events during archaeal evolution and gene sets of ancestral forms. The Last Archaeal Common Ancestor (LACA is conservatively estimated to possess 996 genes compared to 1245 and 1335 genes for the last common ancestors of Crenarchaeota and Euryarchaeota, respectively. It is inferred that LACA was a chemoautotrophic hyperthermophile

  4. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

    Science.gov (United States)

    Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

    2014-01-01

    Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

  5. Integrated predictive modeling of high-mode tokamak plasmas using a combination of core and pedestal models

    International Nuclear Information System (INIS)

    Bateman, Glenn; Bandres, Miguel A.; Onjun, Thawatchai; Kritz, Arnold H.; Pankin, Alexei

    2003-01-01

    A new integrated modeling protocol is developed using a model for the temperature and density pedestal at the edge of high-mode (H-mode) plasmas [Onjun et al., Phys. Plasmas 9, 5018 (2002)] together with the Multi-Mode core transport model (MMM95) [Bateman et al., Phys. Plasmas 5, 1793 (1998)] in the BALDUR integrated modeling code to predict the temperature and density profiles of 33 H-mode discharges. The pedestal model is used to provide the boundary conditions in the simulations, once the heating power rises above the H-mode power threshold. Simulations are carried out for 20 discharges in the Joint European Torus and 13 discharges in the DIII-D tokamak. These discharges include systematic scans in normalized gyroradius, plasma pressure, collisionality, isotope mass, elongation, heating power, and plasma density. The average rms deviation between experimental data and the predicted profiles of temperature and density, normalized by central values, is found to be about 10%. It is found that the simulations tend to overpredict the temperature profiles in discharges with low heating power per plasma particle and to underpredict the temperature profiles in discharges with high heating power per particle. Variations of the pedestal model are used to test the sensitivity of the simulation results

  6. Integration of climate change in flood prediction: application to the Somme river (France)

    Science.gov (United States)

    Pinault, J.-L.; Amraoui, N.; Noyer, M.-L.

    2003-04-01

    Exceptional floods that have occurred for the last two years in western and central Europe were very unlikely. The concomitance of such rare events shows that they might be imputable to climate change. The statistical analysis of long rainfall series confirms that both the cumulated annual height and the temporal variability have increased for the last decade. This paper is devoted to the analysis of climate change impact on flood prediction applied to the Somme river. The exceptional pluviometry that occurred from October 2000 to April 2001, about the double of the mean value, entailed catastrophic flood between the high Somme and Abbeville. The flow reached a peak at the beginning of May 2001, involving damages in numerous habitations and communication routes, and economical activity of the region had been flood-bound for more than 2 months. The flood caught unaware the population and caused deep traumas in France since it was the first time such a sudden event was recognized as resulting from groundwater discharge. Mechanisms of flood generation were studied tightly in order to predict the behavior of the Somme catchment and other urbanized basins when the pluviometry is exceptional in winter or in spring, which occurs more and more frequently in the northern part of Europe. The contribution of groundwater in surface water flow was calculated by inverse modeling from piezometers that are representative of aquifers in valleys. They were found on the slopes and near the edge of plateaus in order to characterize the drainage processes of the watertable to the surface water network. For flood prediction, a stochastic process is used, consisting in the generation of both rainfall and PET time series. The precipitation generator uses Markov chain Monte Carlo and simulated annealing from the Hastings -- Metropolis algorithm. Coupling of rainfall and PET generators with transfer enables a new evaluation of the probability of occurrence of floods, taking into account

  7. Real-Time Prediction of Observed Action Requires Integrity of the Dorsal Premotor Cortex: Evidence From Repetitive Transcranial Magnetic Stimulation.

    Science.gov (United States)

    Brich, Louisa F M; Bächle, Christine; Hermsdörfer, Joachim; Stadler, Waltraud

    2018-01-01

    Studying brain mechanisms underlying the prediction of observed action, the dorsal premotor cortex (PMd) has been suggested a key area. The present study probed this notion using repetitive transcranial magnetic stimulation (rTMS) to test whether interference in this area would affect the accuracy in predicting the time course of object directed actions performed with the right hand. Young and healthy participants observed actions in short videos. These were briefly occluded from view for 600 ms and resumed immediately afterwards. The task was to continue the action mentally and to indicate after each occlusion, whether the action was resumed at the right moment (condition in-time) or shifted. In a first run, single-pulse transcranial magnetic stimulation (sTMS) was delivered over the left primary hand-area during occlusion. In the second run, rTMS over the left PMd was applied during occlusion in half of the participants [experimental group (EG)]. The control group (CG) received sham-rTMS over the same area. Under rTMS, the EG predicted less trials correctly than in the sTMS run. Sham-rTMS in the CG had no effects on prediction. The interference in PMd interacted with the type of manipulation applied to the action's time course occasionally during occlusion. The performance decrease of the EG was most pronounced in conditions in which the continuations after occlusions were too late in the action's course. The present results extend earlier findings suggesting that real-time action prediction requires the integrity of the PMd. Different functional roles of this area are discussed. Alternative interpretations consider either simulation of specific motor programming functions or the involvement of a feature-unspecific predictor.

  8. Real-Time Prediction of Observed Action Requires Integrity of the Dorsal Premotor Cortex: Evidence From Repetitive Transcranial Magnetic Stimulation

    Directory of Open Access Journals (Sweden)

    Louisa F. M. Brich

    2018-03-01

    Full Text Available Studying brain mechanisms underlying the prediction of observed action, the dorsal premotor cortex (PMd has been suggested a key area. The present study probed this notion using repetitive transcranial magnetic stimulation (rTMS to test whether interference in this area would affect the accuracy in predicting the time course of object directed actions performed with the right hand. Young and healthy participants observed actions in short videos. These were briefly occluded from view for 600 ms and resumed immediately afterwards. The task was to continue the action mentally and to indicate after each occlusion, whether the action was resumed at the right moment (condition in-time or shifted. In a first run, single-pulse transcranial magnetic stimulation (sTMS was delivered over the left primary hand-area during occlusion. In the second run, rTMS over the left PMd was applied during occlusion in half of the participants [experimental group (EG]. The control group (CG received sham-rTMS over the same area. Under rTMS, the EG predicted less trials correctly than in the sTMS run. Sham-rTMS in the CG had no effects on prediction. The interference in PMd interacted with the type of manipulation applied to the action’s time course occasionally during occlusion. The performance decrease of the EG was most pronounced in conditions in which the continuations after occlusions were too late in the action’s course. The present results extend earlier findings suggesting that real-time action prediction requires the integrity of the PMd. Different functional roles of this area are discussed. Alternative interpretations consider either simulation of specific motor programming functions or the involvement of a feature-unspecific predictor.

  9. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

    Science.gov (United States)

    Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

    2017-12-04

    Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

  10. Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model.

    Science.gov (United States)

    Hardiansyah, Deni; Attarwala, Ali Asgar; Kletting, Peter; Mottaghy, Felix M; Glatting, Gerhard

    2017-10-01

    To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model. PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of 111 In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq 68 Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P 1 ), 4h p.i. (P 2 ) and the combination of P 1 and P 2 (P 3 ) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of 90 Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v. For P 1 and P 2 the population variabilities of kidneys, liver and spleen were acceptable (v10%). Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  11. Integrated predictive maintenance program vibration and lube oil analysis: Part I - history and the vibration program

    Energy Technology Data Exchange (ETDEWEB)

    Maxwell, H.

    1996-12-01

    This paper is the first of two papers which describe the Predictive Maintenance Program for rotating machines at the Palo Verde Nuclear Generating Station. The organization has recently been restructured and significant benefits have been realized by the interaction, or {open_quotes}synergy{close_quotes} between the Vibration Program and the Lube Oil Analysis Program. This paper starts with the oldest part of the program - the Vibration Program and discusses the evolution of the program to its current state. The {open_quotes}Vibration{close_quotes} view of the combined program is then presented.

  12. Integrated predictive maintenance program vibration and lube oil analysis: Part I - history and the vibration program

    International Nuclear Information System (INIS)

    Maxwell, H.

    1996-01-01

    This paper is the first of two papers which describe the Predictive Maintenance Program for rotating machines at the Palo Verde Nuclear Generating Station. The organization has recently been restructured and significant benefits have been realized by the interaction, or open-quotes synergyclose quotes between the Vibration Program and the Lube Oil Analysis Program. This paper starts with the oldest part of the program - the Vibration Program and discusses the evolution of the program to its current state. The open-quotes Vibrationclose quotes view of the combined program is then presented

  13. Predicting nurses' use of healthcare technology using the technology acceptance model: an integrative review.

    Science.gov (United States)

    Strudwick, Gillian

    2015-05-01

    The benefits of healthcare technologies can only be attained if nurses accept and intend to fully use them. One of the most common models utilized to understand user acceptance of technology is the Technology Acceptance Model. This model and modified versions of it have only recently been applied in the healthcare literature among nurse participants. An integrative literature review was conducted on this topic. Ovid/MEDLINE, PubMed, Google Scholar, and CINAHL were searched yielding a total of 982 references. Upon eliminating duplicates and applying the inclusion and exclusion criteria, the review included a total of four dissertations, three symposium proceedings, and 13 peer-reviewed journal articles. These documents were appraised and reviewed. The results show that a modified Technology Acceptance Model with added variables could provide a better explanation of nurses' acceptance of healthcare technology. These added variables to modified versions of the Technology Acceptance Model are discussed, and the studies' methodologies are critiqued. Limitations of the studies included in the integrative review are also examined.

  14. Cloning of the cDNA for murine von Willebrand factor and identification of orthologous genes reveals the extent of conservation among diverse species.

    Science.gov (United States)

    Chitta, Mohan S; Duhé, Roy J; Kermode, John C

    2007-05-01

    Interaction of von Willebrand factor (VWF) with circulating platelets promotes hemostasis when a blood vessel is injured. The A1 domain of VWF is responsible for the initial interaction with platelets and is well conserved among species. Knowledge of the cDNA and genomic DNA sequences for human VWF allowed us to predict the cDNA sequence for murine VWF in silico and amplify its entire coding region by RT-PCR. The murine VWF cDNA has an open reading frame of 8,442 bp, encoding a protein of 2,813 amino acid residues with 83% identity to human pre-pro-VWF. The same strategy was used to predict in silico the cDNA sequence for the ortholog of VWF in a further six species. Many of these predictions diverged substantially from the putative Reference Sequences derived by ab initio methods. Our predicted sequences indicated that the VWF gene has a conserved structure of 52 exons in all seven mammalian species examined, as well as in the chicken. There is a minor structural variation in the pufferfish Takifugu rubripes insofar as the VWF gene in this species has 53 exons. Comparison of the translated amino acid sequences also revealed a high degree of conservation. In particular, the cysteine residues are conserved precisely throughout both the pro-peptide and the mature VWF sequence in all species, with a minor exception in the pufferfish VWF ortholog where two adjacent cysteine residues are omitted. The marked conservation of cysteine residues emphasizes the importance of the intricate pattern of disulfide bonds in governing the structure of pro-VWF and regulating the function of the mature VWF protein. It should also be emphasized that many of the conserved features of the VWF gene and protein were obscured when the comparison among species was based on the putative Reference Sequences instead of our predicted cDNA sequences.

  15. A novel approach for predicting the response of the spectrometer for INTEGRAL satellite.

    Science.gov (United States)

    Kshetri, R

    2013-05-01

    A basic phenomenological approach has been presented in three recent papers (Kshetri R., 2012. JINST 7, P04008; Kshetri R., 2012. JINST 7, P07006; Kshetri R., 2012. JINST 7, P12007) for understanding the operation of encapsulated type composite detectors including the SPI spectrometer. In the present paper, we have considered the fact that the experimental two-fold events between two detectors include the three and higher fold events between the same two detectors. The formalism has been further developed and the peak-to-total ratio of a general composite detector are predicted for energy region with no direct experimental information about them. At 8MeV, the peak-to-total ratio for the SPI spectrometer and a very large detector (comprising of infinite number of single HPGe modules) are found to be 9% and 12%, respectively. The predictions for fold distribution of the SPI spectrometer are found to be in agreement with experimental data. Our formulation does not include ad-hoc fits, but expressions that are justifiable by probability flow arguments. Instead of using an empirical method or simulation, we present a novel approach for calculating the peak-to-total ratio of the SPI spectrometer for high gamma energies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Prediction of sedimentation using integration of RS, RUSLE model and GIS in Cameron Highlands, Pahang, Malaysia

    Science.gov (United States)

    Ghani, A. H. A.; Lihan, T.; Rahim, S. A.; Musthapha, M. A.; Idris, W. M. R.; Rahman, Z. A.

    2013-11-01

    Soil erosion and sediment yield are strongly affected by land use change. Spatially distributed erosion models are of great interest to predict soil erosion loss and sediment yield. Hence, the objective of this study was to determine sediment yield using Revised Universal Soil Loss Equation (RUSLE) model in Geographical Information System (GIS) environment at Cameron Highlands, Pahang, Malaysia. Sediment yield at the study area was determined using RUSLE model in GIS environment The RUSLE factors were computed by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using soil map and field measurement, vegetation cover (C) using satellite images, length and steepness (LS) using contour map and conservation practices using satellite images based on land use/land cover. Field observations were also done to verify the predicted sediment yield. The results indicated that the rate of sediment yield in the study area ranged from very low to extremely high. The higher SY value can be found at middle and lower catchments of Cameron Highland. Meanwhile, the lower SY value can be found at the north part of the study area. Sediment yield value turned out to be higher close to the river due to the topographic characteristic, vegetation type and density, climate and land use within the drainage basin.

  17. Improved cyberinfrastructure for integrated hydrometeorological predictions within the fully-coupled WRF-Hydro modeling system

    Science.gov (United States)

    gochis, David; hooper, Rick; parodi, Antonio; Jha, Shantenu; Yu, Wei; Zaslavsky, Ilya; Ganapati, Dinesh

    2014-05-01

    The community WRF-Hydro system is currently being used in a variety of flood prediction and regional hydroclimate impacts assessment applications around the world. Despite its increasingly wide use certain cyberinfrastructure bottlenecks exist in the setup, execution and post-processing of WRF-Hydro model runs. These bottlenecks result in wasted time, labor, data transfer bandwidth and computational resource use. Appropriate development and use of cyberinfrastructure to setup and manage WRF-Hydro modeling applications will streamline the entire workflow of hydrologic model predictions. This talk will present recent advances in the development and use of new open-source cyberinfrastructure tools for the WRF-Hydro architecture. These tools include new web-accessible pre-processing applications, supercomputer job management applications and automated verification and visualization applications. The tools will be described successively and then demonstrated in a set of flash flood use cases for recent destructive flood events in the U.S. and in Europe. Throughout, an emphasis on the implementation and use of community data standards for data exchange is made.

  18. Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

    Science.gov (United States)

    Chen, Yu-Jen; Liu, Chih-Min; Hsu, Yung-Chin; Lo, Yu-Chun; Hwang, Tzung-Jeng; Hwu, Hai-Gwo; Lin, Yi-Tin; Tseng, Wen-Yih Isaac

    2018-01-01

    A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  19. Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.

    Directory of Open Access Journals (Sweden)

    Sharad Shandilya

    Full Text Available The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR, rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA technique.358 defibrillations were evaluated (218 unsuccessful and 140 successful. Non-linear properties (Lyapunov exponent > 0 of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity outperformed AMSA (53.6% sensitivity. At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations

  20. Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.

    Directory of Open Access Journals (Sweden)

    Mika Gustafsson

    Full Text Available BACKGROUND: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. METHODOLOGY/PRINCIPAL FINDINGS: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the "elastic net". Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. CONCLUSIONS/SIGNIFICANCE: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

  1. An approximation to the adaptive exponential integrate-and-fire neuron model allows fast and predictive fitting to physiological data

    Directory of Open Access Journals (Sweden)

    Loreen eHertäg

    2012-09-01

    Full Text Available For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ('in-vivo-like' input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a 'high-throughput' model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

  2. SRGULL - AN ADVANCED ENGINEERING MODEL FOR THE PREDICTION OF AIRFRAME INTEGRATED SCRAMJET CYCLE PERFORMANCE

    Science.gov (United States)

    Walton, J. T.

    1994-01-01

    The development of a single-stage-to-orbit aerospace vehicle intended to be launched horizontally into low Earth orbit, such as the National Aero-Space Plane (NASP), has concentrated on the use of the supersonic combustion ramjet (scramjet) propulsion cycle. SRGULL, a scramjet cycle analysis code, is an engineer's tool capable of nose-to-tail, hydrogen-fueled, airframe-integrated scramjet simulation in a real gas flow with equilibrium thermodynamic properties. This program facilitates initial estimates of scramjet cycle performance by linking a two-dimensional forebody, inlet and nozzle code with a one-dimensional combustor code. Five computer codes (SCRAM, SEAGUL, INLET, Progam HUD, and GASH) originally developed at NASA Langley Research Center in support of hypersonic technology are integrated in this program to analyze changing flow conditions. The one-dimensional combustor code is based on the combustor subroutine from SCRAM and the two-dimensional coding is based on an inviscid Euler program (SEAGUL). Kinetic energy efficiency input for sidewall area variation modeling can be calculated by the INLET program code. At the completion of inviscid component analysis, Program HUD, an integral boundary layer code based on the Spaulding-Chi method, is applied to determine the friction coefficient which is then used in a modified Reynolds Analogy to calculate heat transfer. Real gas flow properties such as flow composition, enthalpy, entropy, and density are calculated by the subroutine GASH. Combustor input conditions are taken from one-dimensionalizing the two-dimensional inlet exit flow. The SEAGUL portions of this program are limited to supersonic flows, but the combustor (SCRAM) section can handle supersonic and dual-mode operation. SRGULL has been compared to scramjet engine tests with excellent results. SRGULL was written in FORTRAN 77 on an IBM PC compatible using IBM's FORTRAN/2 or Microway's NDP386 F77 compiler. The program is fully user interactive, but can

  3. With a little help from my goals: integrating intergoal facilitation with the theory of planned behaviour to predict physical activity.

    Science.gov (United States)

    Presseau, Justin; Sniehotta, Falko F; Francis, Jill J; Gebhardt, Winifred A

    2010-11-01

    Integration of a multiple goal theory approach into the theory of planned behaviour (TPB) to investigate how the perceived facilitating and conflicting relationships in multiple goal pursuit predict performance of a health-related behaviour. Prospective design with 8-week follow-up. At baseline, perceived intergoal facilitation and intergoal conflict were measured using personal projects analysis supplemented with standard TPB measures for physical activity (PA). Self-reported PA was measured at follow-up 8 weeks later. N=137 participants completed measures at both time points (55.4% response rate at follow-up). Hierarchical regression showed that perceived intergoal facilitation, but not intergoal conflict, directly predicted PA beyond intention and perceived behavioural control (PBC), accounting for more than 4% of additional variance in PA. Intergoal facilitation had an indirect effect on intention through attitude and PBC, and intention partially mediated the effect of intergoal facilitation on behaviour. The perceived facilitating effect of pursuing other personal goals predicts the performance of a health-related behaviour over and above single behaviour-focused social cognitions.

  4. Fast and General Method To Predict the Physicochemical Properties of Druglike Molecules Using the Integral Equation Theory of Molecular Liquids.

    Science.gov (United States)

    Palmer, David S; Mišin, Maksim; Fedorov, Maxim V; Llinas, Antonio

    2015-09-08

    We report a method to predict physicochemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D reference interaction site model of the integral equation theory of molecular liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark data sets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.

  5. BWR full integral simulation test (FIST) pretest predictions with TRACBO2

    International Nuclear Information System (INIS)

    Sutherland, W.A.; Alamgir, M.

    1984-01-01

    The Full Integral Simulation Test program is a three pronged approach to the development of best-estimate analysis capability for BWR systems. An analytical method development program is underway to extend the BWR-TRAC computer code to model reactor kinetics and major interfacing systems, including balance-of-plant, to improve application modeling flexibility, and to reduce computer running time. An experimental program is underway in a new single bundle system test facility to extend the large break loss-of-coolant accident LOCA data base to small breaks and operational transients. And a method qualification program is underway to test TRACBO2 against experiments in the FIST facility. The recently completed Phase 1 period included a series of LOCA and power transient tests, and successful pretest analysis of the large and small break LOCA tests with TRACBO2. These comparisons demonstrate BWR-TRAC capability for small and large break analysis, and provide detailed understanding of the phenomena

  6. Communication: Predictive partial linearized path integral simulation of condensed phase electron transfer dynamics

    International Nuclear Information System (INIS)

    Huo, Pengfei; Miller, Thomas F. III; Coker, David F.

    2013-01-01

    A partial linearized path integral approach is used to calculate the condensed phase electron transfer (ET) rate by directly evaluating the flux-flux/flux-side quantum time correlation functions. We demonstrate for a simple ET model that this approach can reliably capture the transition between non-adiabatic and adiabatic regimes as the electronic coupling is varied, while other commonly used semi-classical methods are less accurate over the broad range of electronic couplings considered. Further, we show that the approach reliably recovers the Marcus turnover as a function of thermodynamic driving force, giving highly accurate rates over four orders of magnitude from the normal to the inverted regimes. We also demonstrate that the approach yields accurate rate estimates over five orders of magnitude of inverse temperature. Finally, the approach outlined here accurately captures the electronic coherence in the flux-flux correlation function that is responsible for the decreased rate in the inverted regime

  7. The visual air quality predicted by conventional and scanning teleradiometers and an integrating nephelometer

    Energy Technology Data Exchange (ETDEWEB)

    Malm, W [U.S. Environmental Protection Agency, Las Vegas, NV; Pitchford, A; Tree, R; Walther, E; Pearson, M; Archer, S

    1981-12-01

    Many Class I areas have unique vistas which require an observer to look over complex terrain containing basins, valleys, and canyons. These topographic features tend to form pollution ''basins'' and ''corridors'' that trap and funnel air pollutants under certain meteorological conditions. For example, on numerous days, layers of haze in the San Juan River Basin obscure various vista elements including the Chuska Mountains as viewed from Mesa Verde National Park, CO. Measrements by an integrating nephelometer and conventional teleradiometer at one location in Mesa Verde do not quantify inhomogeneities. In this paper, data from these instruments are compated to data derived from scanning teleradiometer measurements of photographic slide images. The slides, surrogates of the real three-dimensional scene, were projected and scanned to determine relative sky and vista radiance at 40 points within a vertical slice of the vista. Comparison of the corresponding visual range data sets for each instrument for September and December 1979 demonstrates the utility of the scanning teleradiometer.

  8. BOG: R-package for Bacterium and virus analysis of Orthologous Groups

    Directory of Open Access Journals (Sweden)

    Jincheol Park

    2015-01-01

    Full Text Available BOG (Bacterium and virus analysis of Orthologous Groups is a package for identifying groups of differentially regulated genes in the light of gene functions for various virus and bacteria genomes. It is designed to identify Clusters of Orthologous Groups (COGs that are enriched among genes that have gone through significant changes under different conditions. This would contribute to the detection of pathogens, an important scientific research area of relevance in uncovering bioterrorism, among others. Particular statistical analyses include hypergeometric, Mann–Whitney rank sum, and gene set enrichment. Results from the analyses are organized and presented in tabular and graphical forms for ease of understanding and dissemination of results. BOG is implemented as an R-package, which is available from CRAN or can be downloaded from http://www.stat.osu.edu/~statgen/SOFTWARE/BOG/.

  9. Market Integration Predicts Human Gut Microbiome Attributes across a Gradient of Economic Development.

    Science.gov (United States)

    Stagaman, Keaton; Cepon-Robins, Tara J; Liebert, Melissa A; Gildner, Theresa E; Urlacher, Samuel S; Madimenos, Felicia C; Guillemin, Karen; Snodgrass, J Josh; Sugiyama, Lawrence S; Bohannan, Brendan J M

    2018-01-01

    Economic development is marked by dramatic increases in the incidence of microbiome-associated diseases, such as autoimmune diseases and metabolic syndromes, but the lifestyle changes that drive alterations in the human microbiome are not known. We measured market integration as a proxy for economically related lifestyle attributes, such as ownership of specific market goods that index degree of market integration and components of traditional and nontraditional (more modern) house structure and infrastructure, and profiled the fecal microbiomes of 213 participants from a contiguous, indigenous Ecuadorian population. Despite relatively modest differences in lifestyle across the population, greater economic development correlated with significantly lower within-host diversity, higher between-host dissimilarity, and a decrease in the relative abundance of the bacterium Prevotella . These microbiome shifts were most strongly associated with more modern housing, followed by reduced ownership of traditional subsistence lifestyle-associated items. IMPORTANCE Previous research has reported differences in the gut microbiome between populations residing in wealthy versus poorer countries, leading to the assertion that lifestyle changes associated with economic development promote changes in the gut microbiome that promote the proliferation of microbiome-associated diseases. However, a direct relationship between economic development and the gut microbiome has not previously been shown. We surveyed the gut microbiomes of a single indigenous population undergoing economic development and found significant associations between features of the gut microbiome and lifestyle changes associated with economic development. These findings suggest that even the earliest stages of economic development can drive changes in the gut microbiome, which may provide a warning sign for the development of microbiome-associated diseases.

  10. Esophageal Baseline Impedance Reflects Mucosal Integrity and Predicts Symptomatic Outcome With Proton Pump Inhibitor Treatment.

    Science.gov (United States)

    Xie, Chenxi; Sifrim, Daniel; Li, Yuwen; Chen, Minhu; Xiao, Yinglian

    2018-01-30

    Esophageal baseline impedance, which is decreased in gastroesophageal reflux disease (GERD) patients, is related to the severity of acid reflux and the integrity of the esophageal mucosa. The study aims to compare the baseline impedance and the dilated intercellular spaces (DIS) within patients with typical reflux symptoms and to evaluate the correlation of baseline impedance with DIS, esophageal acid exposure, as well as the efficacy of proton pump inhibitor (PPI) treatment. Ninety-two patients and 10 healthy controls were included in the study. Erosive esophagitis (EE) was defined by esophageal mucosal erosion under upper endoscopy. Patients without mucosa erosion were divided into groups with pathologic acid reflux (non-erosive reflux disease [NERD]) or with hypersensitive esophagus. The biopsies of esophageal mucosa were taken 2-4 cm above the gastroesophageal junction Z-line during upper endoscopy for DIS measurement. All the patients received esomeprazole 20 mg twice-daily treatment for 8 weeks. The efficacy of esomeprazole was evaluated among all patients. The intercellular spaces were dilated in both EE and NERD patients ( P baseline impedance was decreased in both EE patients and NERD patients, and negatively correlated to the acid exposure time ( r = -0.527, P baseline impedance ( r = -0.230, P Baseline impedance > 1764 Ω" was an independent predictor for PPI failure (OR, 11.9; 95% CI, 2.4-58.9; P baseline impedance was observed in patients with mucosa erosion or pathological acid reflux. The baseline impedance reflected the mucosal integrity, it was more sensitive to esophageal acid exposure. Patients with high impedance might not benefit from the PPI treatment.

  11. From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

    Science.gov (United States)

    Martinez-Millana, A; Fernandez-Llatas, C; Sacchi, L; Segagni, D; Guillen, S; Bellazzi, R; Traver, V

    2015-08-01

    The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

  12. Characterization of the Drosophila ortholog of the human Usher Syndrome type 1G protein sans.

    Directory of Open Access Journals (Sweden)

    Fabio Demontis

    Full Text Available BACKGROUND: The Usher syndrome (USH is the most frequent deaf-blindness hereditary disease in humans. Deafness is attributed to the disorganization of stereocilia in the inner ear. USH1, the most severe subtype, is associated with mutations in genes encoding myosin VIIa, harmonin, cadherin 23, protocadherin 15, and sans. Myosin VIIa, harmonin, cadherin 23, and protocadherin 15 physically interact in vitro and localize to stereocilia tips in vivo, indicating that they form functional complexes. Sans, in contrast, localizes to vesicle-like structures beneath the apical membrane of stereocilia-displaying hair cells. How mutations in sans result in deafness and blindness is not well understood. Orthologs of myosin VIIa and protocadherin 15 have been identified in Drosophila melanogaster and their genetic analysis has identified essential roles in auditory perception and microvilli morphogenesis, respectively. PRINCIPAL FINDINGS: Here, we have identified and characterized the Drosophila ortholog of human sans. Drosophila Sans is expressed in tubular organs of the embryo, in lens-secreting cone cells of the adult eye, and in microvilli-displaying follicle cells during oogenesis. Sans mutants are viable, fertile, and mutant follicle cells appear to form microvilli, indicating that Sans is dispensable for fly development and microvilli morphogenesis in the follicle epithelium. In follicle cells, Sans protein localizes, similar to its vertebrate ortholog, to intracellular punctate structures, which we have identified as early endosomes associated with the syntaxin Avalanche. CONCLUSIONS: Our work is consistent with an evolutionary conserved function of Sans in vesicle trafficking. Furthermore it provides a significant basis for further understanding of the role of this Usher syndrome ortholog in development and disease.

  13. Senataxin, the ortholog of a yeast RNA helicase, is mutant in ataxia-ocular apraxia 2.

    Science.gov (United States)

    Moreira, Maria-Céu; Klur, Sandra; Watanabe, Mitsunori; Németh, Andrea H; Le Ber, Isabelle; Moniz, José-Carlos; Tranchant, Christine; Aubourg, Patrick; Tazir, Meriem; Schöls, Lüdger; Pandolfo, Massimo; Schulz, Jörg B; Pouget, Jean; Calvas, Patrick; Shizuka-Ikeda, Masami; Shoji, Mikio; Tanaka, Makoto; Izatt, Louise; Shaw, Christopher E; M'Zahem, Abderrahim; Dunne, Eimear; Bomont, Pascale; Benhassine, Traki; Bouslam, Naïma; Stevanin, Giovanni; Brice, Alexis; Guimarães, João; Mendonça, Pedro; Barbot, Clara; Coutinho, Paula; Sequeiros, Jorge; Dürr, Alexandra; Warter, Jean-Marie; Koenig, Michel

    2004-03-01

    Ataxia-ocular apraxia 2 (AOA2) was recently identified as a new autosomal recessive ataxia. We have now identified causative mutations in 15 families, which allows us to clinically define this entity by onset between 10 and 22 years, cerebellar atrophy, axonal sensorimotor neuropathy, oculomotor apraxia and elevated alpha-fetoprotein (AFP). Ten of the fifteen mutations cause premature termination of a large DEAxQ-box helicase, the human ortholog of yeast Sen1p, involved in RNA maturation and termination.

  14. Proteinortho: Detection of (Co-)orthologs in large-scale analysis

    OpenAIRE

    Lechner, Marcus; Findeiß, Sven; Steiner, Lydia; Marz, Manja; Stadler, Peter F; Prohaska, Sonja J

    2011-01-01

    Abstract Background Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes i...

  15. SITEX 2.0: Projections of protein functional sites on eukaryotic genes. Extension with orthologous genes.

    Science.gov (United States)

    Medvedeva, Irina V; Demenkov, Pavel S; Ivanisenko, Vladimir A

    2017-04-01

    Functional sites define the diversity of protein functions and are the central object of research of the structural and functional organization of proteins. The mechanisms underlying protein functional sites emergence and their variability during evolution are distinguished by duplication, shuffling, insertion and deletion of the exons in genes. The study of the correlation between a site structure and exon structure serves as the basis for the in-depth understanding of sites organization. In this regard, the development of programming resources that allow the realization of the mutual projection of exon structure of genes and primary and tertiary structures of encoded proteins is still the actual problem. Previously, we developed the SitEx system that provides information about protein and gene sequences with mapped exon borders and protein functional sites amino acid positions. The database included information on proteins with known 3D structure. However, data with respect to orthologs was not available. Therefore, we added the projection of sites positions to the exon structures of orthologs in SitEx 2.0. We implemented a search through database using site conservation variability and site discontinuity through exon structure. Inclusion of the information on orthologs allowed to expand the possibilities of SitEx usage for solving problems regarding the analysis of the structural and functional organization of proteins. Database URL: http://www-bionet.sscc.ru/sitex/ .

  16. wALADin benzimidazoles differentially modulate the function of porphobilinogen synthase orthologs.

    Science.gov (United States)

    Lentz, Christian S; Halls, Victoria S; Hannam, Jeffrey S; Strassel, Silke; Lawrence, Sarah H; Jaffe, Eileen K; Famulok, Michael; Hoerauf, Achim; Pfarr, Kenneth M

    2014-03-27

    The heme biosynthesis enzyme porphobilinogen synthase (PBGS) is a potential drug target in several human pathogens. wALADin1 benzimidazoles have emerged as species-selective PBGS inhibitors against Wolbachia endobacteria of filarial worms. In the present study, we have systematically tested wALADins against PBGS orthologs from bacteria, protozoa, metazoa, and plants to elucidate the inhibitory spectrum. However, the effect of wALADin1 on different PBGS orthologs was not limited to inhibition: several orthologs were stimulated by wALADin1; others remained unaffected. We demonstrate that wALADins allosterically modulate the PBGS homooligomeric equilibrium with inhibition mediated by favoring low-activity oligomers, while 5-aminolevulinic acid, Mg(2+), or K(+) stabilized high-activity oligomers. Pseudomonas aeruginosa PBGS could be inhibited or stimulated by wALADin1 depending on these factors and pH. We have defined the wALADin chemotypes responsible for either inhibition or stimulation, facilitating the design of tailored PBGS modulators for potential application as antimicrobial agents, herbicides, or drugs for porphyric disorders.

  17. Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

    Science.gov (United States)

    Neufeld, K. N.; Keinath, A. P.; Gugino, B. K.; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

    2017-11-01

    Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good

  18. Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

    Science.gov (United States)

    Penny, Stephen G.; Akella, Santha; Buehner, Mark; Chevallier, Matthieu; Counillon, Francois; Draper, Clara; Frolov, Sergey; Fujii, Yosuke; Karspeck, Alicia; Kumar, Arun

    2017-01-01

    The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust

  19. INTEGRATION OF QSAR AND SAR METHODS FOR THE MECHANISTIC INTERPRETATION OF PREDICTIVE MODELS FOR CARCINOGENICITY

    Directory of Open Access Journals (Sweden)

    Natalja Fjodorova

    2012-07-01

    Full Text Available The knowledge-based Toxtree expert system (SAR approach was integrated with the statistically based counter propagation artificial neural network (CP ANN model (QSAR approach to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals.

  20. Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction.

    Science.gov (United States)

    Dimitrakopoulos, Lampros; Prassas, Ioannis; Diamandis, Eleftherios P; Charames, George S

    2017-09-01

    The overall goal of translational oncology is to identify molecular alterations indicative of cancer or of responsiveness to specific therapeutic regimens. While next-generation sequencing has played a pioneering role in this quest, the latest advances in proteomic technologies promise to provide a holistic approach to the further elucidation of tumor biology. Genetic information may be written in DNA and flow from DNA to RNA to protein, according to the central dogma of molecular biology, but the observed phenotype is dictated predominantly by the DNA protein coding region-derived proteotype. Proteomics holds the potential to bridge the gap between genotype and phenotype, because the powerful analytical tool of mass spectrometry has reached a point of maturity to serve this purpose effectively. This integration of "omics" data has given birth to the novel field of onco-proteogenomics, which has much to offer to precision medicine and personalized patient management. Here, we review briefly how each "omics" technology has individually contributed to cancer research, discuss technological and computational advances that have contributed to the realization of onco-proteogenomics, and summarize current and future translational applications.

  1. Sensory processing patterns predict the integration of information held in visual working memory.

    Science.gov (United States)

    Lowe, Matthew X; Stevenson, Ryan A; Wilson, Kristin E; Ouslis, Natasha E; Barense, Morgan D; Cant, Jonathan S; Ferber, Susanne

    2016-02-01

    Given the limited resources of visual working memory, multiple items may be remembered as an averaged group or ensemble. As a result, local information may be ill-defined, but these ensemble representations provide accurate diagnostics of the natural world by combining gist information with item-level information held in visual working memory. Some neurodevelopmental disorders are characterized by sensory processing profiles that predispose individuals to avoid or seek-out sensory stimulation, fundamentally altering their perceptual experience. Here, we report such processing styles will affect the computation of ensemble statistics in the general population. We identified stable adult sensory processing patterns to demonstrate that individuals with low sensory thresholds who show a greater proclivity to engage in active response strategies to prevent sensory overstimulation are less likely to integrate mean size information across a set of similar items and are therefore more likely to be biased away from the mean size representation of an ensemble display. We therefore propose the study of ensemble processing should extend beyond the statistics of the display, and should also consider the statistics of the observer. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  2. A case study for the integration of predictive mineral potential maps

    Science.gov (United States)

    Lee, Saro; Oh, Hyun-Joo; Heo, Chul-Ho; Park, Inhye

    2014-09-01

    This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

  3. Global Integration of the Hot-State Brain Network of Appetite Predicts Short Term Weight Loss in Older Adult

    Directory of Open Access Journals (Sweden)

    Brielle M Paolini

    2015-05-01

    Full Text Available Obesity is a public health crisis in North America. While lifestyle interventions for weight loss (WL remain popular, the rate of success is highly variable. Clearly, self-regulation of eating behavior is a challenge and patterns of activity across the brain may be an important determinant of success. The current study prospectively examined whether integration across the Hot-State Brain Network of Appetite (HBN-A predicts WL after 6-months of treatment in older adults. Our metric for network integration was global efficiency (GE. The present work is a sub-study (n = 56 of an ongoing randomized clinical trial involving WL. Imaging involved a baseline food-cue visualization functional MRI (fMRI scan following an overnight fast. Using graph theory to build functional brain networks, we demonstrated that regions of the HBN-A (insula, anterior cingulate cortex (ACC, superior temporal pole, amygdala and the parahippocampal gyrus were highly integrated as evidenced by the results of a principal component analysis. After accounting for known correlates of WL (baseline weight, age, sex, and self-regulatory efficacy and treatment condition, which together contributed 36.9% of the variance in WL, greater GE in the HBN-A was associated with an additional 19% of the variance. The ACC of the HBN-A was the primary driver of this effect, accounting for 14.5% of the variance in WL when entered in a stepwise regression following the covariates, p = 0.0001. The HBN-A is comprised of limbic regions important in the processing of emotions and visceral sensations and the ACC is key for translating such processing into behavioral consequences. The improved integration of these regions may enhance awareness of body and emotional states leading to more successful self-regulation and to greater WL. This is the first study among older adults to prospectively demonstrate that, following an overnight fast, GE of the HBN-A during a food visualization task is predictive of

  4. The Left, The Better: White-Matter Brain Integrity Predicts Foreign Language Imitation Ability.

    Science.gov (United States)

    Vaquero, Lucía; Rodríguez-Fornells, Antoni; Reiterer, Susanne M

    2017-08-01

    Speech imitation is crucial for language acquisition and second-language learning. Interestingly, large individual differences regarding the ability in imitating foreign-language sounds have been observed. The origin of this interindividual diversity remains unknown, although it might be partially explained by structural predispositions. Here we correlated white-matter structural properties of the arcuate fasciculus (AF) with the performance of 52 German-speakers in a Hindi sentence- and word-imitation task. First, a manual reconstruction was performed, permitting us to extract the mean values along the three branches of the AF. We found that a larger lateralization of the AF volume toward the left hemisphere predicted the performance of our participants in the imitation task. Second, an automatic reconstruction was carried out, allowing us to localize the specific region within the AF that exhibited the largest correlation with foreign language imitation. Results of this reconstruction also showed a left lateralization trend: greater fractional anisotropy values in the anterior half of the left AF correlated with the performance in the Hindi-imitation task. From the best of our knowledge, this is the first time that foreign language imitation aptitude is tested using a more ecological imitation task and correlated with DTI tractography, using both a manual and an automatic method. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  5. Modelling oil-shale integrated tri-generator behaviour: predicted performance and financial assessment

    International Nuclear Information System (INIS)

    Jaber, J.O.; Probert, S.D.; Williams, P.T.

    1998-01-01

    A simple theoretical model relating the inputs and outputs of the proposed process has been developed; the main objectives being to predict the final products (i.e. the production rates for liquid and gaseous fuels as well as electricity), the total energy-conversion efficiency and the incurred costs under various operating conditions. The tri-production concept involves the use of a circulating fluidised-bed combustor together with a gasifier, retort and simple combined-cycle plant. The mathematical model requires mass and energy balances to be undertaken: these are based on the scarce published data about retorting as well as fluidised-bed combustion and gasification of oil shale. A prima facie case is made that the proposed tri-production plant provides an attractive and economic means for producing synthetic fuels and electricity from oil shale. The unit cost of electricity, so generated, would at present be about 0.057 US$ per kWh, assuming a 10% annual interest charge on the invested capital. If the produced shale oil could be sold for more than 25 US$ per barrel, then the cost of the generated electricity would be appropriately less and hence more competitive. (author)

  6. Modelling oil-shale integrated tri-generator behaviour: predicted performance and financial assessment

    Energy Technology Data Exchange (ETDEWEB)

    Jaber, J.O.; Probert, S.D. [Cranfield University, Bedford (United Kingdom). School of Mechanical Engineering; Williams, P.T. [Leeds University (United Kingdom). Dept. of Fuel and Energy

    1998-02-01

    A simple theoretical model relating the inputs and outputs of the proposed process has been developed; the main objectives being to predict the final products (i.e. the production rates for liquid and gaseous fuels as well as electricity), the total energy-conversion efficiency and the incurred costs under various operating conditions. The tri-production concept involves the use of a circulating fluidised-bed combustor together with a gasifier, retort and simple combined-cycle plant. The mathematical model requires mass and energy balances to be undertaken: these are based on the scarce published data about retorting as well as fluidised-bed combustion and gasification of oil shale. A prima facie case is made that the proposed tri-production plant provides an attractive and economic means for producing synthetic fuels and electricity from oil shale. The unit cost of electricity, so generated, would at present be about 0.057 US$ per kWh, assuming a 10% annual interest charge on the invested capital. If the produced shale oil could be sold for more than 25 US$ per barrel, then the cost of the generated electricity would be appropriately less and hence more competitive. (author)

  7. Modelling oil-shale integrated tri-generator behaviour: predicted performance and financial assessment

    Energy Technology Data Exchange (ETDEWEB)

    Jaber, J.O.; Probert, S.D. [Cranfield University, Bedford (United Kingdom). School of Mechanical Engineering; Williams, P.T. [Leeds University (United Kingdom). Dept. of Fuel and Energy

    1998-03-01

    A simple theoretical model relating the inputs and outputs of the proposed process has been developed; the main objectives being to predict the final products (i.e., the production rates for liquid and gaseous fuels as well as electricity), the total energy-conversion efficiency and the incurred costs under various operating conditions. The tri-production concept involves the use of a circulating fluidised-bed combustor together with a gasifier, retort and simple combined-cycle plant. The mathematical model requires mass and energy balances to be undertaken: these are based on the scarce published data about retorting as well as fluidised-bed combustion and gasification of oilshale. A prima facie case is made that the proposed tri-production plant provides an attractive and economic means for producing synthetic fuels and electricity from oil shale. The unit cost of electricity, so generated, would at present be about 0.057 US$ per kWh, assuming a 10% annual interest charge on the invested capital. If the produced shale oil could be sold for more than 25 US$ per barrel, then the cost of the generated electricity would be appropriately less and hence more competitive. (author)

  8. Pressure integration technique for predicting wind-induced response in high-rise buildings

    Directory of Open Access Journals (Sweden)

    Aly Mousaad Aly

    2013-12-01

    Full Text Available This paper presents a procedure for response prediction in high-rise buildings under wind loads. The procedure is illustrated in an application example of a tall building exposed to both cross-wind and along-wind loads. The responses of the building in the lateral directions combined with torsion are estimated simultaneously. Results show good agreement with recent design standards; however, the proposed procedure has the advantages of accounting for complex mode shapes, non-uniform mass distribution, and interference effects from the surrounding. In addition, the technique allows for the contribution of higher modes. For accurate estimation of the acceleration response, it is important to consider not only the first two lateral vibrational modes, but also higher modes. Ignoring the contribution of higher modes may lead to underestimation of the acceleration response; on the other hand, it could result in overestimation of the displacement response. Furthermore, the procedure presented in this study can help decision makers, involved in a tall building design/retrofit to choose among innovative solutions like aerodynamic mitigation, structural member size adjustment, damping enhancement, and/or materials change, with an objective to improve the resiliency and the serviceability under extreme wind actions.

  9. Mapping and predicting sinkholes by integration of remote sensing and spectroscopy methods

    Science.gov (United States)

    Goldshleger, N.; Basson, U.; Azaria, I.

    2013-08-01

    The Dead Sea coastal area is exposed to the destructive process of sinkhole collapse. The increase in sinkhole activity in the last two decades has been substantial, resulting from the continuous decrease in the Dead Sea's level, with more than 1,000 sinkholes developing as a result of upper layer collapse. Large sinkholes can reach 25 m in diameter. They are concentrated mainly in clusters in several dozens of sites with different characteristics. In this research, methods for mapping, monitoring and predicting sinkholes were developed using active and passive remote-sensing methods: field spectrometer, geophysical ground penetration radar (GPR) and a frequency domain electromagnetic instrument (FDEM). The research was conducted in three stages: 1) literature review and data collection; 2) mapping regions abundant with sinkholes in various stages and regions vulnerable to sinkholes; 3) analyzing the data and translating it into cognitive and accessible scientific information. Field spectrometry enabled a comparison between the spectral signatures of soil samples collected near active or progressing sinkholes, and those collected in regions with no visual sign of sinkhole occurrence. FDEM and GPR investigations showed that electrical conductivity and soil moisture are higher in regions affected by sinkholes. Measurements taken at different time points over several seasons allowed monitoring the progress of an 'embryonic' sinkhole.

  10. On the prediction of the reactor vessel integrity under severe accident loadings (RPVSA)

    Energy Technology Data Exchange (ETDEWEB)

    Krieg, R. E-mail: maeule@irs.fzk.de; Devos, J.; Caroli, C.; Solomos, G.; Ennis, P.J.; Kalkhof, D

    2001-11-01

    In order to allow more reliable predictions on the lower head response under core melt-down conditions, the temperature distribution has been analysed including the natural convection in the corium pool. Furthermore, the mechanical models and the failure criteria have been improved based on the RUPTHER and FASTHER experiments where typical temperature gradients are simulated. Lower head local melting as well as corium crust development has been addressed in the CORVIS experiments studying the contact between an alumina/iron thermite and a thick steel plate. The upper head loading by corium impact due to a postulated in-vessel steam explosion has been investigated by the BERDA experiments. Similarity rules were considered such that the results can be directly converted to reactor conditions. Based on these investigations admissible steam explosion energy releases are determined which the upper head can carry. If these limits are not exceeded the reactor containment cannot be endangered by broken head fragments. To provide the necessary basic data, mechanical material tests have been performed.

  11. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

    Directory of Open Access Journals (Sweden)

    Xingli Guo

    Full Text Available The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.

  12. The integral biologically effective dose to predict brain stem toxicity of hypofractionated stereotactic radiotherapy

    International Nuclear Information System (INIS)

    Clark, Brenda G.; Souhami, Luis; Pla, Conrado; Al-Amro, Abdullah S.; Bahary, Jean-Paul; Villemure, Jean-Guy; Caron, Jean-Louis; Olivier, Andre; Podgorsak, Ervin B.

    1998-01-01

    Purpose: The aim of this work was to develop a parameter for use during fractionated stereotactic radiotherapy treatment planning to aid in the determination of the appropriate treatment volume and fractionation regimen that will minimize risk of late damage to normal tissue. Materials and Methods: We have used the linear quadratic model to assess the biologically effective dose at the periphery of stereotactic radiotherapy treatment volumes that impinge on the brain stem. This paper reports a retrospective study of 77 patients with malignant and benign intracranial lesions, treated between 1987 and 1995, with the dynamic rotation technique in 6 fractions over a period of 2 weeks, to a total dose of 42 Gy prescribed at the 90% isodose surface. From differential dose-volume histograms, we evaluated biologically effective dose-volume histograms and obtained an integral biologically-effective dose (IBED) in each case. Results: Of the 77 patients in the study, 36 had target volumes positioned so that the brain stem received more than 1% of the prescribed dose, and 4 of these, all treated for meningioma, developed serious late damage involving the brain stem. Other than type of lesion, the only significant variable was the volume of brain stem exposed. An analysis of the IBEDs received by these 36 patients shows evidence of a threshold value for late damage to the brain stem consistent with similar thresholds that have been determined for external beam radiotherapy. Conclusions: We have introduced a new parameter, the IBED, that may be used to represent the fractional effective dose to structures such as the brain stem that are partially irradiated with stereotactic dose distributions. The IBED is easily calculated prior to treatment and may be used to determine appropriate treatment volumes and fractionation regimens minimizing possible toxicity to normal tissue

  13. Evaluation and prediction of oil biodegradation: a novel approach integrating geochemical and basin modeling techniques in offshore Brazil

    Energy Technology Data Exchange (ETDEWEB)

    Baudino, Roger [YPF S.A. (Argentina); Santos, Glauce Figueiredo dos; Losilla, Carlos; Cabrera, Ricardo; Loncarich, Ariel; Gavarrino, Alejandro [RepsolYPF do Brasil, Sao Paulo, SP (Brazil)

    2008-07-01

    Oil fields accounting for a large portion of the world reserves are severely affected by biological degradation. In Brazil, giant fields of the Campos Basin are producing biodegraded oils with widely variable fluid characteristics (10 to 40 deg API) and no apparent logical distribution nor predictability. Modern geochemical techniques allow defining the level of biodegradation. When original (non-degraded) oil samples and other with varying degradation level are available it might be possible to define a distribution trend and to relate it to present day geological factors such as temperature and reservoir geometry. However, other critical factors must be taken into account. But most of all, it is fundamental to have a vision in time of their evolution. This can only be achieved through 3D Basin Models coupled with modern visualization tools. The multi-disciplinary work-flow described here integrates three-dimensional numerical simulations with modern geochemical analyses. (author)

  14. Performance of an integrated approach for prediction of bond dissociation enthalpies of phenols extracted from ginger and tea

    Science.gov (United States)

    Nam, Pham Cam; Chandra, Asit K.; Nguyen, Minh Tho

    2013-01-01

    Integration of the (RO)B3LYP/6-311++G(2df,2p) with the PM6 method into a two-layer ONIOM is found to produce reasonably accurate BDE(O-H)s of phenolic compounds. The chosen ONIOM model contains only two atoms of the breaking bond as the core zone and is able to provide reliable evaluation for BDE(O-H) for phenols and tocopherol. Deviation of calculated values from experiment is ±(1-2) kcal/mol. BDE(O-H) of several curcuminoids and flavanoids extracted from ginger and tea are computed using the proposed model. The BDE(O-H) values of enol curcumin and epigallocatechin gallate are predicted to be 83.3 ± 2.0 and 76.0 ± 2.0 kcal/mol, respectively.

  15. Corticospinal integrity and motor impairment predict outcomes after excitatory repetitive transcranial magnetic stimulation: a preliminary study.

    Science.gov (United States)

    Lai, Chih-Jou; Wang, Chih-Pin; Tsai, Po-Yi; Chan, Rai-Chi; Lin, Shan-Hui; Lin, Fu-Gong; Hsieh, Chin-Yi

    2015-01-01

    To identify the effective predictors for therapeutic outcomes based on intermittent theta-burst stimulation (iTBS). A sham-controlled, double-blind parallel study design. A tertiary hospital. People with stroke (N=72) who presented with unilateral hemiplegia. Ten consecutive sessions of real or sham iTBS were implemented with the aim of enhancing hand function. Patients were categorized into 4 groups according to the presence (MEP+) or absence (MEP-) of motor-evoked potentials (MEPs) and grip strength according to the Medical Research Council (MRC) scale. Cortical excitability, Wolf Motor Function Test (WMFT), finger-tapping task (FT), and simple reaction time were performed before and after the sessions. MEPs and the MRC scale were predictive of iTBS therapeutic outcomes. Group A (MEP+, MRC>1) exhibited the greatest WMFT change (7.6±2.3, P1; 5.2±2.2 score change) and group C (MEP-, MRC=0; 2.3±1.5 score change). These improvements were correlated significantly with baseline motor function and ipsilesional maximum MEP amplitude. The effectiveness of iTBS modulation for poststroke motor enhancement depends on baseline hand grip strength and the presence of MEPs. Our findings indicate that establishing neurostimulation strategies based on the proposed electrophysiological and clinical criteria can allow iTBS to be executed with substantial precision. Effective neuromodulatory strategies can be formulated by using electrophysiological features and clinical presentation information as guidelines. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  16. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  17. 'Integration'

    DEFF Research Database (Denmark)

    Olwig, Karen Fog

    2011-01-01

    , while the countries have adopted disparate policies and ideologies, differences in the actual treatment and attitudes towards immigrants and refugees in everyday life are less clear, due to parallel integration programmes based on strong similarities in the welfare systems and in cultural notions...... of equality in the three societies. Finally, it shows that family relations play a central role in immigrants’ and refugees’ establishment of a new life in the receiving societies, even though the welfare society takes on many of the social and economic functions of the family....

  18. Toward an integrated quasi-operational air quality analysis and prediction system for South America

    Science.gov (United States)

    Hoshyaripour, Gholam Ali; Brasseur, Guy; Petersen, Katinka; Bouarar, Idiir; Andrade, Maria de Fatima

    2015-04-01

    Recent industrialization and urbanization in South America (SA) have notably exacerbated the air pollution with adverse impacts on human health and socio-economic systems. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality at different temporal and spatial scales and minimize its damages. Based on previous achievements (e.g., MACC project in Europe and PANDA project in East Asia) we aim to design and implement an integrated system to monitor, analyze and forecast the air quality in SA along with its impacts upon public health and agriculture. An initiative will be established to combine observations (both satellite and in-situ) with advanced numerical models in order to provide a robust scientific basis for short- and long-term decision-making concerning air quality issues in SA countries. The main objectives of the project are defined as 3E: Enhancement of the air quality monitoring system through coupling models and observations, Elaboration of comprehensive indicators and assessment tools to support policy-making, Establishment of efficient information-exchange platforms to facilitate communication among scientists, authorities, stockholders and the public. Here we present the results of the initial stage, where a coarse resolution (50×50 km) set up of Weather Research and Forecast model with Chemistry (WRF-Chem) is used to simulate the air quality in SA considering anthropogenic, biomass-burning (based on MACCity, FINN inventories, respectively) and biogenic emissions (using MEGAN model). According to the availability of the observation data for Metropolitan Area of São Paulo, August 2012 is selected as the simulation period. Nested domains with higher resolution (15×15 km) are also embedded within the parent domain over the megacities (Sao Paolo and Rio de Janeiro in Brazil and Buenos Aires in Argentina), which account for the major anthropogenic emission sources located along coastal regions

  19. Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation

    Energy Technology Data Exchange (ETDEWEB)

    Fakra, A.H., E-mail: fakra@univ-reunion.f [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France); Miranville, F.; Boyer, H.; Guichard, S. [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France)

    2011-07-15

    Research highlights: {yields} This study presents a new model capable to simulate indoor daylighting. {yields} The model was introduced in research software called CODYRUN. {yields} The validation of the code was realized from a lot of tests cases. -- Abstract: Many models exist in the scientific literature for determining indoor daylighting values. They are classified in three categories: numerical, simplified and empirical models. Nevertheless, each of these categories of models are not convenient for every application. Indeed, the numerical model requires high calculation time; conditions of use of the simplified models are limited, and experimental models need not only important financial resources but also a perfect control of experimental devices (e.g. scale model), as well as climatic characteristics of the location (e.g. in situ experiment). In this article, a new model based on a combination of multiple simplified models is established. The objective is to improve this category of model. The originality of our paper relies on the coupling of several simplified models of indoor daylighting calculations. The accuracy of the simulation code, introduced into CODYRUN software to simulate correctly indoor illuminance, is then verified. Besides, the software consists of a numerical building simulation code, developed in the Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT) at the University of Reunion. Initially dedicated to the thermal, airflow and hydrous phenomena in the buildings, the software has been completed for the calculation of indoor daylighting. New models and algorithms - which rely on a semi-detailed approach - will be presented in this paper. In order to validate the accuracy of the integrated models, many test cases have been considered as analytical, inter-software comparisons and experimental comparisons. In order to prove the accuracy of the new model - which can properly simulate the illuminance - a

  20. Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

    Science.gov (United States)

    Roberts, Dar A.; Church, Richard; Ustin, Susan L.; Brass, James A. (Technical Monitor)

    2001-01-01

    Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated

  1. Verification of geomechanical integrity and prediction of long-term mineral trapping for the Ketzin CO2 storage pilot site

    Science.gov (United States)

    Kempka, Thomas; De Lucia, Marco; Kühn, Michael

    2014-05-01

    Static and dynamic numerical modelling generally accompany the entire CO2 storage site life cycle. Thereto, it is required to match the employed models with field observations on a regular basis in order to predict future site behaviour. We investigated the coupled processes at the Ketzin CO2 storage pilot site [1] using a model coupling concept focusing on the temporal relevance of processes involved (hydraulic, chemical and mechanical) at given time-scales (site operation, abandonment and long-term stabilization). For that purpose, long-term dynamic multi-phase flow simulations [2], [3] established the basis for all simulations discussed in the following. Hereby, pressure changes resulting in geomechanical effects are largest during site operation, whereas geochemical reactions are governed by slow kinetics resulting in a long-term stabilization. To account for mechanical integrity, which may be mainly affected during site operation, we incorporated a regional-scale coupled hydro-mechanical model. Our simulation results show maximum ground surface displacements of about 4 mm, whereas shear and tensile failure are not observed. Consequently, the CO2 storage operation at the Ketzin pilot site does not compromise reservoir, caprock and fault integrity. Chemical processes responsible for mineral trapping are expected to mainly occur during long-term stabilization at the Ketzin pilot site [4]. Hence, our previous assessment [3] was extended by integrating two long-term mineral trapping scenarios. Thereby, mineral trapping contributes to the trapping mechanisms with 11.7 % after 16,000 years of simulation in our conservative and with 30.9 % in our maximum reactivity scenarios. Dynamic flow simulations indicate that only 0.2 % of the CO2 injected (about 67,270 t CO2 in total) is in gaseous state, but structurally trapped after 16,000 years. Depending on the studied long-term scenario, CO2 dissolution is the dominating trapping mechanism with 68.9 % and 88

  2. The effects of stimulus modality and task integrality: Predicting dual-task performance and workload from single-task levels

    Science.gov (United States)

    Hart, S. G.; Shively, R. J.; Vidulich, M. A.; Miller, R. C.

    1986-01-01

    The influence of stimulus modality and task difficulty on workload and performance was investigated. The goal was to quantify the cost (in terms of response time and experienced workload) incurred when essentially serial task components shared common elements (e.g., the response to one initiated the other) which could be accomplished in parallel. The experimental tasks were based on the Fittsberg paradigm; the solution to a SternBERG-type memory task determines which of two identical FITTS targets are acquired. Previous research suggested that such functionally integrated dual tasks are performed with substantially less workload and faster response times than would be predicted by suming single-task components when both are presented in the same stimulus modality (visual). The physical integration of task elements was varied (although their functional relationship remained the same) to determine whether dual-task facilitation would persist if task components were presented in different sensory modalities. Again, it was found that the cost of performing the two-stage task was considerably less than the sum of component single-task levels when both were presented visually. Less facilitation was found when task elements were presented in different sensory modalities. These results suggest the importance of distinguishing between concurrent tasks that complete for limited resources from those that beneficially share common resources when selecting the stimulus modalities for information displays.

  3. Integrated 18F-FDG PET/MRI in breast cancer. Early prediction of response to neoadjuvant chemotherapy

    International Nuclear Information System (INIS)

    Cho, Nariya; Im, Seock-Ah; Lee, Kyung-Hun; Kim, Tae-Yong; Cheon, Gi Jeong; Park, In-Ae; Kim, Young Seon; Kwon, Bo Ra; Lee, Jung Min; Suh, Hoon Young; Suh, Koung Jin

    2018-01-01

    To explore whether integrated 18 F-FDG PET/MRI can be used to predict pathological response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Between November 2014 and April 2016, 26 patients with breast cancer who had received NAC and subsequent surgery were prospectively enrolled. Each patient underwent 18 F-FDG PET/MRI examination before and after the first cycle of NAC. Qualitative MRI parameters, including morphological descriptors and the presence of peritumoral oedema were assessed. Quantitatively, PET parameters, including maximum standardized uptake value, metabolic tumour volume and total lesion glycolysis (TLG), and MRI parameters, including washout proportion and signal enhancement ratio (SER), were measured. The performance of the imaging parameters singly and in combination in predicting a pathological incomplete response (non-pCR) was assessed. Of the 26 patients, 7 (26.9%) exhibited a pathological complete response (pCR), and 19 (73.1%) exhibited a non-pCR. No significant differences were found between the pCR and non-pCR groups in the qualitative MRI parameters. The mean percentage reductions in TLG 30% on PET and SER on MRI were significantly greater in the pCR group than in the non-pCR group (TLG 30% -64.8 ± 15.5% vs. -25.4 ± 48.7%, P = 0.005; SER -34.6 ± 19.7% vs. -8.7 ± 29.0%, P = 0.040). The area under the receiver operating characteristic curve for the percentage change in TLG 30% (0.789, 95% CI 0.614 to 0.965) was similar to that for the percentage change in SER (0.789, 95% CI 0.552 to 1.000; P = 1.000). The specificity of TLG 30% in predicting pCR was 100% (7/7) and that of SER was 71.4% (5/7). The sensitivity of TLG 30% in predicting non-pCR was 63.2% (12/19) and that of SER was 84.2% (16/19). When the combined TLG 30% and SER criterion was applied, sensitivity was 100% (19/19), and specificity was 71.4% (5/7). 18 F-FDG PET/MRI can be used to predict non-pCR after the first cycle of NAC in patients with breast cancer

  4. Integrated {sup 18}F-FDG PET/MRI in breast cancer. Early prediction of response to neoadjuvant chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Nariya [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Seoul National University Medical Research Center, Institute of Radiation Medicine, Seoul (Korea, Republic of); Im, Seock-Ah; Lee, Kyung-Hun; Kim, Tae-Yong [Seoul National University College of Medicine, Department of Internal Medicine, Seoul (Korea, Republic of); Seoul National University, Cancer Research Institute, Seoul (Korea, Republic of); Cheon, Gi Jeong [Seoul National University, Cancer Research Institute, Seoul (Korea, Republic of); Seoul National University Hospital, Department of Nuclear Medicine, Seoul (Korea, Republic of); Park, In-Ae [Seoul National University College of Medicine, Department of Pathology, Seoul (Korea, Republic of); Kim, Young Seon [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Yeungnam University, Department of Radiology, College of Medicine, Daegu (Korea, Republic of); Kwon, Bo Ra [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Lee, Jung Min; Suh, Hoon Young [Seoul National University Hospital, Department of Nuclear Medicine, Seoul (Korea, Republic of); Suh, Koung Jin [Seoul National University College of Medicine, Department of Internal Medicine, Seoul (Korea, Republic of)

    2018-03-15

    To explore whether integrated {sup 18}F-FDG PET/MRI can be used to predict pathological response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Between November 2014 and April 2016, 26 patients with breast cancer who had received NAC and subsequent surgery were prospectively enrolled. Each patient underwent {sup 18}F-FDG PET/MRI examination before and after the first cycle of NAC. Qualitative MRI parameters, including morphological descriptors and the presence of peritumoral oedema were assessed. Quantitatively, PET parameters, including maximum standardized uptake value, metabolic tumour volume and total lesion glycolysis (TLG), and MRI parameters, including washout proportion and signal enhancement ratio (SER), were measured. The performance of the imaging parameters singly and in combination in predicting a pathological incomplete response (non-pCR) was assessed. Of the 26 patients, 7 (26.9%) exhibited a pathological complete response (pCR), and 19 (73.1%) exhibited a non-pCR. No significant differences were found between the pCR and non-pCR groups in the qualitative MRI parameters. The mean percentage reductions in TLG{sub 30%} on PET and SER on MRI were significantly greater in the pCR group than in the non-pCR group (TLG{sub 30%} -64.8 ± 15.5% vs. -25.4 ± 48.7%, P = 0.005; SER -34.6 ± 19.7% vs. -8.7 ± 29.0%, P = 0.040). The area under the receiver operating characteristic curve for the percentage change in TLG{sub 30%} (0.789, 95% CI 0.614 to 0.965) was similar to that for the percentage change in SER (0.789, 95% CI 0.552 to 1.000; P = 1.000). The specificity of TLG{sub 30%} in predicting pCR was 100% (7/7) and that of SER was 71.4% (5/7). The sensitivity of TLG{sub 30%} in predicting non-pCR was 63.2% (12/19) and that of SER was 84.2% (16/19). When the combined TLG{sub 30%} and SER criterion was applied, sensitivity was 100% (19/19), and specificity was 71.4% (5/7). {sup 18}F-FDG PET/MRI can be used to predict non-pCR after the first

  5. An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

    International Nuclear Information System (INIS)

    Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula; Heinzle, Elmar

    2009-01-01

    Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac, tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC 50 values 100 μM or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.

  6. Predictive Direct Torque Control Application-Specific Integrated Circuit of an Induction Motor Drive with a Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Guo-Ming Sung

    2017-06-01

    Full Text Available This paper proposes a modified predictive direct torque control (PDTC application-specific integrated circuit (ASIC of a motor drive with a fuzzy controller for eliminating sampling and calculating delay times in hysteresis controllers. These delay times degrade the control quality and increase both torque and flux ripples in a motor drive. The proposed fuzzy PDTC ASIC calculates the stator’s magnetic flux and torque by detecting the three-phase current, three-phase voltage, and rotor speed, and eliminates the ripples in the torque and flux by using a fuzzy controller and predictive scheme. The Verilog hardware description language was used to implement the hardware architecture, and the ASIC was fabricated by the Taiwan Semiconductor Manufacturing Company through a 0.18-μm 1P6M CMOS process that involved a cell-based design method. The measurements revealed that the proposed fuzzy PDTC ASIC of the three-phase induction motor yielded a test coverage of 96.03%, fault coverage of 95.06%, chip area of 1.81 × 1.81 mm2, and power consumption of 296 mW, at an operating frequency of 50 MHz and a supply voltage of 1.8 V.

  7. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

    Directory of Open Access Journals (Sweden)

    István A Kovács

    Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

  8. The Predictive Value of Integrated Pulmonary Index after Off-Pump Coronary Artery Bypass Grafting: A Prospective Observational Study

    Directory of Open Access Journals (Sweden)

    Evgenia V. Fot

    2017-08-01

    Full Text Available BackgroundThe early warning scores may increase the safety of perioperative period. The objective of this study was to assess the diagnostic and predictive role of Integrated Pulmonary Index (IPI after off-pump coronary artery bypass grafting (OPCAB.Materials and MethodsForty adult patients undergoing elective OPCAB were enrolled into a single-center prospective observational study. We assessed respiratory function using IPI that includes oxygen saturation, end-tidal CO2, respiratory rate, and pulse rate. In addition, we evaluated blood gas analyses and hemodynamics, including ECG, invasive arterial pressure, and cardiac index. The measurements were performed after transfer to the intensive care unit, after spontaneous breathing trial and at 2, 6, 12, and 18 h after extubation.Results and DiscussionThe value of IPI registered during respiratory support correlated weakly with cardiac index (rho = 0.4; p = 0.04 and ScvO2 (rho = 0.4, p = 0.02. After extubation, IPI values decreased significantly, achieving a minimum by 18 h. The IPI value ≤9 at 6 h after extubation was a predictor of complicated early postoperative period (AUC = 0.71; p = 0.04 observed in 13 patients.ConclusionIn off-pump coronary surgery, the IPI decreases significantly after tracheal extubation and may predict postoperative complications.

  9. Systematic discovery of unannotated genes in 11 yeast species using a database of orthologous genomic segments

    LENUS (Irish Health Repository)

    OhEigeartaigh, Sean S

    2011-07-26

    Abstract Background In standard BLAST searches, no information other than the sequences of the query and the database entries is considered. However, in situations where two genes from different species have only borderline similarity in a BLAST search, the discovery that the genes are located within a region of conserved gene order (synteny) can provide additional evidence that they are orthologs. Thus, for interpreting borderline search results, it would be useful to know whether the syntenic context of a database hit is similar to that of the query. This principle has often been used in investigations of particular genes or genomic regions, but to our knowledge it has never been implemented systematically. Results We made use of the synteny information contained in the Yeast Gene Order Browser database for 11 yeast species to carry out a systematic search for protein-coding genes that were overlooked in the original annotations of one or more yeast genomes but which are syntenic with their orthologs. Such genes tend to have been overlooked because they are short, highly divergent, or contain introns. The key features of our software - called SearchDOGS - are that the database entries are classified into sets of genomic segments that are already known to be orthologous, and that very weak BLAST hits are retained for further analysis if their genomic location is similar to that of the query. Using SearchDOGS we identified 595 additional protein-coding genes among the 11 yeast species, including two new genes in Saccharomyces cerevisiae. We found additional genes for the mating pheromone a-factor in six species including Kluyveromyces lactis. Conclusions SearchDOGS has proven highly successful for identifying overlooked genes in the yeast genomes. We anticipate that our approach can be adapted for study of further groups of species, such as bacterial genomes. More generally, the concept of doing sequence similarity searches against databases to which external

  10. Development and bin mapping of a Rosaceae Conserved Ortholog Set (COS) of markers.

    Science.gov (United States)

    Cabrera, Antonio; Kozik, Alex; Howad, Werner; Arus, Pere; Iezzoni, Amy F; van der Knaap, Esther

    2009-11-29

    Detailed comparative genome analyses within the economically important Rosaceae family have not been conducted. This is largely due to the lack of conserved gene-based molecular markers that are transferable among the important crop genera within the family [e.g. Malus (apple), Fragaria (strawberry), and Prunus (peach, cherry, apricot and almond)]. The lack of molecular markers and comparative whole genome sequence analysis for this family severely hampers crop improvement efforts as well as QTL confirmation and validation studies. We identified a set of 3,818 rosaceaous unigenes comprised of two or more ESTs that correspond to single copy Arabidopsis genes. From this Rosaceae Conserved Orthologous Set (RosCOS), 1039 were selected from which 857 were used for the development of intron-flanking primers and allele amplification. This led to successful amplification and subsequent mapping of 613 RosCOS onto the Prunus TxE reference map resulting in a genome-wide coverage of 0.67 to 1.06 gene-based markers per cM per linkage group. Furthermore, the RosCOS primers showed amplification success rates from 23 to 100% across the family indicating that a substantial part of the RosCOS primers can be directly employed in other less studied rosaceaous crops. Comparisons of the genetic map positions of the RosCOS with the physical locations of the orthologs in the Populus trichocarpa genome identified regions of colinearity between the genomes of Prunus-Rosaceae and Populus-Salicaceae. Conserved orthologous genes are extremely useful for the analysis of genome evolution among closely and distantly related species. The results presented in this study demonstrate the considerable potential of the mapped Prunus RosCOS for genome-wide marker employment and comparative whole genome studies within the Rosaceae family. Moreover, these markers will also function as useful anchor points for the genome sequencing efforts currently ongoing in this family as well as for comparative QTL

  11. New Insights on Eggplant/Tomato/Pepper Synteny and Identification of Eggplant and Pepper Orthologous QTL

    Directory of Open Access Journals (Sweden)

    Riccardo Rinaldi

    2016-07-01

    Full Text Available Eggplant, pepper and tomato are the most exploited berry-producing vegetables within the Solanaceae family. Their genomes differ in size, but each has 12 chromosomes which have undergone rearrangements causing a redistribution of loci. The genome sequences of all three species are available but differ in coverage, assembly quality and percentage of anchorage.Determining their syntenic relationship and QTL orthology will contribute to exploit genomic resources and genetic data for key agronomic traits.The syntenic analysis between tomato and pepper based on the alignment of 34,727 tomato CDS to the pepper genome sequence, identified 19,734 unique hits. The resulting synteny map confirmed the 14 inversions and 10 translocations previously documented, but also highlighted 3 new translocations and 4 major new inversions. Furthermore, each of the 12 chromosomes exhibited a number of rearrangements involving small regions of 0.5-0.7 Mbp.Due to high fragmentation of the publicly available eggplant genome sequence, physical localization of most eggplant QTL was not possible, thus, we compared the organization of the eggplant genetic map with the genome sequence of both tomato and pepper. The eggplant/tomato syntenic map confirmed all the 10 translocations but only 9 of the 14 known inversions; on the other hand, a newly detected inversion was recognized while another one was not confirmed. The eggplant/pepper syntenic map confirmed 10 translocations and 8 inversions already detected and suggested a putative new translocation.In order to perform the assessment of eggplant and pepper QTL orthology, the eggplant and pepper sequence-based markers located in their respective genetic map were aligned onto the pepper genome. GBrowse in pepper was used as reference platform for QTL positioning. A set of 151 pepper QTL were located as well as 212 eggplant QTL, including 76 major QTL (PVE ≥ 10% affecting key agronomic traits. Most were confirmed to cluster in

  12. QuartetS: A Fast and Accurate Algorithm for Large-Scale Orthology Detection

    Science.gov (United States)

    2011-01-01

    of these two genes with all other genes of the other one species. In addition, to be considered orthologs, the BBH pairs had to satisfy two conditions ...BBH pair computations employed as part of the outgroup and QuartetS methods, we used the same two conditions as the ones described above. In our...versus proteins. Genetica , 118, 209–216. 4. Serres,M.H., Kerr,A.R., McCormack,T.J. and Riley,M. (2009) Evolution by leaps: gene duplication in bacteria

  13. License - PGDBj - Ortholog DB | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available List Contact us PGDBj - Ortholog DB License License to Use This Database Last updated : 2017/03/07 You may use this database...cifies the license terms regarding the use of this database and the requirements you must follow in using this database.... The license for this database is specified in the Creative Commons A...ttribution-Share Alike 4.0 International . If you use data from this database, please be sure attribute this database...hare Alike 4.0 International is found here . With regard to this database, you are licensed to: freely acces

  14. An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction

    International Nuclear Information System (INIS)

    Zheng, Xiujuan; Fang, Huajing

    2015-01-01

    The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regression (RVR) and applied to RUL and short-term capacity prediction of batteries. A RVR model is employed as a nonlinear time-series prediction model to predict the UKF future residuals which otherwise remain zero during the prediction period. Taking the prediction step into account, the predictive value through the RVR method and the latest real residual value constitute the future evolution of the residuals with a time-varying weighting scheme. Next, the future residuals are utilized by UKF to recursively estimate the battery parameters for predicting RUL and short-term capacity. Finally, the performance of the proposed method is validated and compared to other predictors with the experimental data. According to the experimental and analysis results, the proposed approach has high reliability and prediction accuracy, which can be applied to battery monitoring and prognostics, as well as generalized to other prognostic applications. - Highlights: • An integrated method is proposed for RUL prediction as well as short-term capacity prediction. • Relevance vector regression model is employed as a nonlinear time-series prediction model. • Unscented Kalman filter is used to recursively update the states for battery model parameters during the prediction. • A time-varying weighting scheme is utilized to improve the accuracy of the RUL prediction. • The proposed method demonstrates high reliability and prediction accuracy.

  15. Integrated genomic and immunophenotypic classification of pancreatic cancer reveals three distinct subtypes with prognostic/predictive significance.

    Science.gov (United States)

    Wartenberg, Martin; Cibin, Silvia; Zlobec, Inti; Vassella, Erik; Eppenberger-Castori, Serenella M M; Terracciano, Luigi; Eichmann, Micha; Worni, Mathias; Gloor, Beat; Perren, Aurel; Karamitopoulou, Eva

    2018-04-16

    Current clinical classification of pancreatic ductal adenocarcinoma (PDAC) is unable to predict prognosis or response to chemo- or immunotherapy and does not take into account the host reaction to PDAC-cells. Our aim is to classify PDAC according to host- and tumor-related factors into clinically/biologically relevant subtypes by integrating molecular and microenvironmental findings. A well-characterized PDAC-cohort (n=110) underwent next-generation sequencing with a hotspot cancer panel, while Next-generation Tissue-Microarrays were immunostained for CD3, CD4, CD8, CD20, PD-L1, p63, hyaluronan-mediated motility receptor (RHAMM) and DNA mismatch-repair proteins. Previous data on FOXP3 were integrated. Immune-cell counts and protein expression were correlated with tumor-derived driver mutations, clinicopathologic features (TNM 8. 2017), survival and epithelial-mesenchymal-transition (EMT)-like tumor budding.  Results: Three PDAC-subtypes were identified: the "immune-escape" (54%), poor in T- and B-cells and enriched in FOXP3+Tregs, with high-grade budding, frequent CDKN2A- , SMAD4- and PIK3CA-mutations and poor outcome; the "immune-rich" (35%), rich in T- and B-cells and poorer in FOXP3+Tregs, with infrequent budding, lower CDKN2A- and PIK3CA-mutation rate and better outcome and a subpopulation with tertiary lymphoid tissue (TLT), mutations in DNA damage response genes (STK11, ATM) and the best outcome; and the "immune-exhausted" (11%) with immunogenic microenvironment and two subpopulations: one with PD-L1-expression and high PIK3CA-mutation rate and a microsatellite-unstable subpopulation with high prevalence of JAK3-mutations. The combination of low budding, low stromal FOXP3-counts, presence of TLTs and absence of CDKN2A-mutations confers significant survival advantage in PDAC-patients. Immune host responses correlate with tumor characteristics leading to morphologically recognizable PDAC-subtypes with prognostic/predictive significance. Copyright ©2018

  16. Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

    Directory of Open Access Journals (Sweden)

    Lemke Ney

    2009-09-01

    Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing

  17. Archaeal Clusters of Orthologous Genes (arCOGs): An Update and Application for Analysis of Shared Features between Thermococcales, Methanococcales, and Methanobacteriales

    OpenAIRE

    Makarova, Kira; Wolf, Yuri; Koonin, Eugene

    2015-01-01

    With the continuously accelerating genome sequencing from diverse groups of archaea and bacteria, accurate identification of gene orthology and availability of readily expandable clusters of orthologous genes are essential for the functional annotation of new genomes. We report an update of the collection of archaeal Clusters of Orthologous Genes (arCOGs) to cover, on average, 91% of the protein-coding genes in 168 archaeal genomes. The new arCOGs were constructed using refined algorithms for...

  18. Leveraging Web Services in Providing Efficient Discovery, Retrieval, and Integration of NASA-Sponsored Observations and Predictions

    Science.gov (United States)

    Bambacus, M.; Alameh, N.; Cole, M.

    2006-12-01

    The Applied Sciences Program at NASA focuses on extending the results of NASA's Earth-Sun system science research beyond the science and research communities to contribute to national priority applications with societal benefits. By employing a systems engineering approach, supporting interoperable data discovery and access, and developing partnerships with federal agencies and national organizations, the Applied Sciences Program facilitates the transition from research to operations in national applications. In particular, the Applied Sciences Program identifies twelve national applications, listed at http://science.hq.nasa.gov/earth-sun/applications/, which can be best served by the results of NASA aerospace research and development of science and technologies. The ability to use and integrate NASA data and science results into these national applications results in enhanced decision support and significant socio-economic benefits for each of the applications. This paper focuses on leveraging the power of interoperability and specifically open standard interfaces in providing efficient discovery, retrieval, and integration of NASA's science research results. Interoperability (the ability to access multiple, heterogeneous geoprocessing environments, either local or remote by means of open and standard software interfaces) can significantly increase the value of NASA-related data by increasing the opportunities to discover, access and integrate that data in the twelve identified national applications (particularly in non-traditional settings). Furthermore, access to data, observations, and analytical models from diverse sources can facilitate interdisciplinary and exploratory research and analysis. To streamline this process, the NASA GeoSciences Interoperability Office (GIO) is developing the NASA Earth-Sun System Gateway (ESG) to enable access to remote geospatial data, imagery, models, and visualizations through open, standard web protocols. The gateway (online

  19. Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production

    Science.gov (United States)

    Zhang, Qingyuan; Middleton, Elizabeth M.; Cheng, Yen-Ben; Huemmrich, K. Fred; Cook, Bruce D.; Corp, Lawrence A.; Kustas, William P.; Russ, Andrew L.; Prueger, John H.; Yao, Tian

    2016-01-01

    The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to e. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol-(sup -1) PPFD and coefficient of variation (CV) of 16

  20. Genomic analysis of NAC transcription factors in banana (Musa acuminata) and definition of NAC orthologous groups for monocots and dicots.

    Science.gov (United States)

    Cenci, Albero; Guignon, Valentin; Roux, Nicolas; Rouard, Mathieu

    2014-05-01

    Identifying the molecular mechanisms underlying tolerance to abiotic stresses is important in crop breeding. A comprehensive understanding of the gene families associated with drought tolerance is therefore highly relevant. NAC transcription factors form a large plant-specific gene family involved in the regulation of tissue development and responses to biotic and abiotic stresses. The main goal of this study was to set up a framework of orthologous groups determined by an expert sequence comparison of NAC genes from both monocots and dicots. In order to clarify the orthologous relationships among NAC genes of different species, we performed an in-depth comparative study of four divergent taxa, in dicots and monocots, whose genomes have already been completely sequenced: Arabidopsis thaliana, Vitis vinifera, Musa acuminata and Oryza sativa. Due to independent evolution, NAC copy number is highly variable in these plant genomes. Based on an expert NAC sequence comparison, we propose forty orthologous groups of NAC sequences that were probably derived from an ancestor gene present in the most recent common ancestor of dicots and monocots. These orthologous groups provide a curated resource for large-scale protein sequence annotation of NAC transcription factors. The established orthology relationships also provide a useful reference for NAC function studies in newly sequenced genomes such as M. acuminata and other plant species.

  1. Analytical solutions for prediction of the ignition time of wood particles based on a time and space integral method

    International Nuclear Information System (INIS)

    Haseli, Y.; Oijen, J.A. van; Goey, L.P.H. de

    2012-01-01

    Highlights: ► A simple model for prediction of the ignition time of a wood particle is presented. ► The formulation is given for both thermally thin and thermally thick particles. ► Transition from thermally thin to thick regime occurs at a critical particle size. ► The model is validated against a numerical model and various experimental data. - Abstract: The main idea of this paper is to establish a simple approach for prediction of the ignition time of a wood particle assuming that the thermo-physical properties remain constant and ignition takes place at a characteristic ignition temperature. Using a time and space integral method, explicit relationships are derived for computation of the ignition time of particles of three common shapes (slab, cylinder and sphere), which may be characterized as thermally thin or thermally thick. It is shown through a dimensionless analysis that the dimensionless ignition time can be described as a function of non-dimensional ignition temperature, reactor temperature or external incident heat flux, and parameter K which represents the ratio of conduction heat transfer to the external radiation heat transfer. The numerical results reveal that for the dimensionless ignition temperature between 1.25 and 2.25 and for values of K up to 8000 (corresponding to woody materials), the variation of the ignition time of a thermally thin particle with K and the dimensionless ignition temperature is linear, whereas the dependence of the ignition time of a thermally thick particle on the above two parameters obeys a quadratic function. Furthermore, it is shown that the transition from the regime of thermally thin to the regime of thermally thick occurs at K cr (corresponding to a critical size of particle) which is found to be independent of the particle shape. The model is validated by comparing the predicted and the measured ignition time of several wood particles obtained from different sources. Good agreement is achieved which

  2. Evaluation Of The Integrated Solubility Model, A Graded Approach For Predicting Phase Distribution In Hanford Tank Waste

    International Nuclear Information System (INIS)

    Pierson, Kayla L.; Belsher, Jeremy D.; Seniow, Kendra R.

    2012-01-01

    The mission of the DOE River Protection Project (RPP) is to store, retrieve, treat and dispose of Hanford's tank waste. Waste is retrieved from the underground tanks and delivered to the Waste Treatment and Immobilization Plant (WTP). Waste is processed through a pretreatment facility where it is separated into low activity waste (LAW), which is primarily liquid, and high level waste (HLW), which is primarily solid. The LAW and HLW are sent to two different vitrification facilities and glass canisters are then disposed of onsite (for LAW) or shipped off-site (for HLW). The RPP mission is modeled by the Hanford Tank Waste Operations Simulator (HTWOS), a dynamic flowsheet simulator and mass balance model that is used for mission analysis and strategic planning. The integrated solubility model (ISM) was developed to improve the chemistry basis in HTWOS and better predict the outcome of the RPP mission. The ISM uses a graded approach to focus on the components that have the greatest impact to the mission while building the infrastructure for continued future improvement and expansion. Components in the ISM are grouped depending upon their relative solubility and impact to the RPP mission. The solubility of each group of components is characterized by sub-models of varying levels of complexity, ranging from simplified correlations to a set of Pitzer equations used for the minimization of Gibbs Energy

  3. Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

    Directory of Open Access Journals (Sweden)

    Chieh-Fan Chen

    2011-01-01

    Full Text Available This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

  4. Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration

    Directory of Open Access Journals (Sweden)

    Thongchart Kerdphol

    2017-05-01

    Full Text Available Renewable energy sources (RESs, such as wind and solar generations, equip inverters to connect to the microgrids. These inverters do not have any rotating mass, thus lowering the overall system inertia. This low system inertia issue could affect the microgrid stability and resiliency in the situation of uncertainties. Today’s microgrids will become unstable if the capacity of RESs become larger and larger, leading to the weakening of microgrid stability and resilience. This paper addresses a new concept of a microgrid control incorporating a virtual inertia system based on the model predictive control (MPC to emulate virtual inertia into the microgrid control loop, thus stabilizing microgrid frequency during high penetration of RESs. The additional controller of virtual inertia is applied to the microgrid, employing MPC with virtual inertia response. System modeling and simulations are carried out using MATLAB/Simulink® software. The simulation results confirm the superior robustness and frequency stabilization effect of the proposed MPC-based virtual inertia control in comparison to the fuzzy logic system and conventional virtual inertia control in a system with high integration of RESs. The proposed MPC-based virtual inertia control is able to improve the robustness and frequency stabilization of the microgrid effectively.

  5. Alexander Technique Training Coupled With an Integrative Model of Behavioral Prediction in Teachers With Low Back Pain.

    Science.gov (United States)

    Kamalikhah, Tahereh; Morowatisharifabad, Mohammad Ali; Rezaei-Moghaddam, Farid; Ghasemi, Mohammad; Gholami-Fesharaki, Mohammad; Goklani, Salma

    2016-09-01

    Individuals suffering from chronic low back pain (CLBP) experience major physical, social, and occupational disruptions. Strong evidence confirms the effectiveness of Alexander technique (AT) training for CLBP. The present study applied an integrative model (IM) of behavioral prediction for improvement of AT training. This was a quasi-experimental study of female teachers with nonspecific LBP in southern Tehran in 2014. Group A contained 42 subjects and group B had 35 subjects. In group A, AT lessons were designed based on IM constructs, while in group B, AT lessons only were taught. The validity and reliability of the AT questionnaire were confirmed using content validity (CVR 0.91, CVI 0.96) and Cronbach's α (0.80). The IM constructs of both groups were measured after the completion of training. Statistical analysis used independent and paired samples t-tests and the univariate generalized linear model (GLM). Significant differences were recorded before and after intervention (P < 0.001) for the model constructs of intention, perceived risk, direct attitude, behavioral beliefs, and knowledge in both groups. Direct attitude and behavioral beliefs in group A were higher than in group B after the intervention (P < 0.03). The educational framework provided by IM for AT training improved attitude and behavioral beliefs that can facilitate the adoption of AT behavior and decreased CLBP.

  6. Integral Analysis of Field Work and Laboratory Electrical Resistivity Imaging for Saline Water Intrusion Prediction in Groundwater

    Science.gov (United States)

    Zawawi, M. H.; Zahar, M. F.; Hashim, M. M. M.; Hazreek, Z. A. M.; Zahari, N. M.; Kamaruddin, M. A.

    2018-04-01

    Saline water intrusion is a serious threat to the groundwater as many part of the world utilize groundwater as their main source of fresh water supply. The usage of high salinity level of water as drinking water can lead to a very serious health hazard towards human. Saline water intrusion is a process by which induced flow of seawater into freshwater aquifer along the coastal area. It might happen due to human action and/or by natural event. The climate change and rise up of sea level may speed up the saline water intrusion process. The conventional method for distinguishing and checking saltwater interference to groundwater along the coast aquifers is to gather and test the groundwater from series of observation wells (borehole) with an end goal to give the important information about the hydrochemistry data to conclude whether the water in the well are safe to consume or not. An integrated approach of field and laboratory electrical resistivity investigation is proposed for indicating the contact region between saline and fresh groundwater. It was found that correlation for both soilbox produced almost identical curvilinear trends for 2% increment of seawater tested using sand sample. This project contributes towards predicting the saline water intrusion to the groundwater by non-destructive test that can replaced the conventional method of groundwater monitoring using series of boreholes in the coastal area

  7. Evaluation Of The Integrated Solubility Model, A Graded Approach For Predicting Phase Distribution In Hanford Tank Waste

    Energy Technology Data Exchange (ETDEWEB)

    Pierson, Kayla L.; Belsher, Jeremy D.; Seniow, Kendra R.

    2012-10-19

    The mission of the DOE River Protection Project (RPP) is to store, retrieve, treat and dispose of Hanford's tank waste. Waste is retrieved from the underground tanks and delivered to the Waste Treatment and Immobilization Plant (WTP). Waste is processed through a pretreatment facility where it is separated into low activity waste (LAW), which is primarily liquid, and high level waste (HLW), which is primarily solid. The LAW and HLW are sent to two different vitrification facilities and glass canisters are then disposed of onsite (for LAW) or shipped off-site (for HLW). The RPP mission is modeled by the Hanford Tank Waste Operations Simulator (HTWOS), a dynamic flowsheet simulator and mass balance model that is used for mission analysis and strategic planning. The integrated solubility model (ISM) was developed to improve the chemistry basis in HTWOS and better predict the outcome of the RPP mission. The ISM uses a graded approach to focus on the components that have the greatest impact to the mission while building the infrastructure for continued future improvement and expansion. Components in the ISM are grouped depending upon their relative solubility and impact to the RPP mission. The solubility of each group of components is characterized by sub-models of varying levels of complexity, ranging from simplified correlations to a set of Pitzer equations used for the minimization of Gibbs Energy.

  8. The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

    Science.gov (United States)

    Koch, J.; Jensen, K. H.; Stisen, S.

    2017-12-01

    Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

  9. Plastic fracture mechanics prediction of fracture instability in a circumferentially cracked pipe in bending - 1. J-integral analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zahoor, A.; Kanninen, M.F.

    1981-11-01

    A method of evaluating the J-integral for a circumferentially cracked pipe in bending is proposed. The method allows a J-resistance curve to be evaluated directly from the load-displacement record obtained in a pipe fracture experiment. It permits an analysis for fracture instability in a circumferential crack growth using a J-resistance curve and the tearing modulus parameter. The influence of the system compliance on fracture instability is discussed in conjunction with the latter application. The importance of using a J-resistance curve that is consistent with the type of constraint for a given application is emphasized. The possibility of a pipe fracture emanating from a stress corrosion crack in the heat-affected zones of girth-welds in Type 304 stainless steel pipes was investigated. The J-resistance curve was employed. A pipe fracture experiment was performed using a spring-loaded four-point bending system that simulated an 8.8-m long section of unsupported 102-mm-dia pipe. An initial through-wall crack of length equal to 104 mm was used. Fracture instability was predicted to occur between 15.2 and 22.1 mm of stable crack growth at each tip. In the actual experiment, the onset of fracture instability occurred beyond maximum load at an average stable crack growth of 11.7 to 19 mm at each tip. 24 refs.

  10. Plastic fracture mechanics prediction of fracture instability in a circumferentially cracked pipe in bending - 1. J-integral analysis

    International Nuclear Information System (INIS)

    Zahoor, A.; Kanninen, M.F.

    1981-01-01

    A method of evaluating the J-integral for a circumferentially cracked pipe in bending is proposed. The method allows a J-resistance curve to be evaluated directly from the load-displacement record obtained in a pipe fracture experiment. It permits an analysis for fracture instability in a circumferential crack growth using a J-resistance curve and the tearing modulus parameter. The influence of the system compliance on fracture instability is discussed in conjunction with the latter application. The importance of using a J-resistance curve that is consistent with the type of constraint for a given application is emphasized. The possibility of a pipe fracture emanating from a stress corrosion crack in the heat-affected zones of girth-welds in Type 304 stainless steel pipes was investigated. The J-resistance curve was employed. A pipe fracture experiment was performed using a spring-loaded four-point bending system that simulated an 8.8-m long section of unsupported 102-mm-dia pipe. An initial through-wall crack of length equal to 104 mm was used. Fracture instability was predicted to occur between 15.2 and 22.1 mm of stable crack growth at each tip. In the actual experiment, the onset of fracture instability occurred beyond maximum load at an average stable crack growth of 11.7 to 19 mm at each tip. 24 refs

  11. A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bangzhu Zhu

    2012-02-01

    Full Text Available Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD, genetic algorithm (GA and artificial neural network (ANN is proposed to forecast carbon price. Firstly, the proposed model uses EMD to decompose carbon price data into several intrinsic mode functions (IMFs and one residue. Then, the IMFs and residue are composed into a high frequency component, a low frequency component and a trend component which have similar frequency characteristics, simple components and strong regularity using the fine-to-coarse reconstruction algorithm. Finally, those three components are predicted using an ANN trained by GA, i.e., a GAANN model, and the final forecasting results can be obtained by the sum of these three forecasting results. For verification and testing, two main carbon future prices with different maturity in the European Climate Exchange (ECX are used to test the effectiveness of the proposed multiscale ensemble forecasting model. Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW, ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARIMA model with EMD preprocessing.

  12. Fast and simple protein-alignment-guided assembly of orthologous gene families from microbiome sequencing reads.

    Science.gov (United States)

    Huson, Daniel H; Tappu, Rewati; Bazinet, Adam L; Xie, Chao; Cummings, Michael P; Nieselt, Kay; Williams, Rohan

    2017-01-25

    Microbiome sequencing projects typically collect tens of millions of short reads per sample. Depending on the goals of the project, the short reads can either be subjected to direct sequence analysis or be assembled into longer contigs. The assembly of whole genomes from metagenomic sequencing reads is a very difficult problem. However, for some questions, only specific genes of interest need to be assembled. This is then a gene-centric assembly where the goal is to assemble reads into contigs for a family of orthologous genes. We present a new method for performing gene-centric assembly, called protein-alignment-guided assembly, and provide an implementation in our metagenome analysis tool MEGAN. Genes are assembled on the fly, based on the alignment of all reads against a protein reference database such as NCBI-nr. Specifically, the user selects a gene family based on a classification such as KEGG and all reads binned to that gene family are assembled. Using published synthetic community metagenome sequencing reads and a set of 41 gene families, we show that the performance of this approach compares favorably with that of full-featured assemblers and that of a recently published HMM-based gene-centric assembler, both in terms of the number of reference genes detected and of the percentage of reference sequence covered. Protein-alignment-guided assembly of orthologous gene families complements whole-metagenome assembly in a new and very useful way.

  13. The Cyclin-Dependent Kinase Ortholog pUL97 of Human Cytomegalovirus Interacts with Cyclins

    Directory of Open Access Journals (Sweden)

    Laura Graf

    2013-12-01

    Full Text Available The human cytomegalovirus (HCMV-encoded protein kinase, pUL97, is considered a cyclin-dependent kinase (CDK ortholog, due to shared structural and functional characteristics. The primary mechanism of CDK activation is binding to corresponding cyclins, including cyclin T1, which is the usual regulatory cofactor of CDK9. This study provides evidence of direct interaction between pUL97 and cyclin T1 using yeast two-hybrid and co-immunoprecipitation analyses. Confocal immunofluorescence revealed partial colocalization of pUL97 with cyclin T1 in subnuclear compartments, most pronounced in viral replication centres. The distribution patterns of pUL97 and cyclin T1 were independent of HCMV strain and host cell type. The sequence domain of pUL97 responsible for the interaction with cyclin T1 was between amino acids 231–280. Additional co-immunoprecipitation analyses showed cyclin B1 and cyclin A as further pUL97 interaction partners. Investigation of the pUL97-cyclin T1 interaction in an ATP consumption assay strongly suggested phosphorylation of pUL97 by the CDK9/cyclin T1 complex in a substrate concentration-dependent manner. This is the first demonstration of interaction between a herpesviral CDK ortholog and cellular cyclins.

  14. Evaluating the Determinants of Sugary Beverage Consumption among Overweight and Obese Adults: An Application of the Integrative Model of Behavioural Prediction

    Science.gov (United States)

    Collado-Rivera, Maria; Branscum, Paul; Larson, Daniel; Gao, Haijuan

    2018-01-01

    Objective: The objective of this study was to evaluate the determinants of sugary drink consumption among overweight and obese adults attempting to lose weight using the Integrative Model of Behavioural Prediction (IMB). Design: Cross-sectional design. Method: Determinants of behavioural intentions (attitudes, perceived norms and perceived…

  15. Integrated Theory of Planned Behavior with Extrinsic Motivation to Predict Intention Not to Use Illicit Drugs by Fifth-Grade Students in Taiwan

    Science.gov (United States)

    Liao, Jung-Yu; Chang, Li-Chun; Hsu, Hsiao-Pei; Huang, Chiu-Mieh; Huang, Su-Fei; Guo, Jong-Long

    2017-01-01

    This study assessed the effects of a model that integrated the theory of planned behavior (TPB) with extrinsic motivation (EM) in predicting the intentions of fifth-grade students to not use illicit drugs. A cluster-sampling design was adopted in a cross-sectional survey (N = 571). The structural equation modeling results showed that the model…

  16. Overexpression of DOSOC1, an ortholog of Arabidopsis SOC1, promotes flowering in the orchid Dendrobium Chao Parya Smile.

    Science.gov (United States)

    Ding, Lihua; Wang, Yanwen; Yu, Hao

    2013-04-01

    SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) encodes a MADS-box protein that plays an essential role in integrating multiple flowering signals to regulate the transition from vegetative to reproductive development in the model plant Arabidopsis. Although SOC1-like genes have been isolated in various angiosperms, its orthologs in Orchidaceae, one of the largest families of flowering plants, are so far unknown. To investigate the regulatory mechanisms of flowering time control in orchids, we isolated a SOC1-like gene, DOSOC1, from Dendrobium Chao Praya Smile. DOSOC1 was highly expressed in reproductive organs, including inflorescence apices, pedicels, floral buds and open flowers. Its expression significantly increased in whole plantlets during the transition from vegetative to reproductive development, which usually occurred after 8 weeks of culture in Dendrobium Chao Praya Smile. In the shoot apex at the floral transitional stage, DOSOC1 was particularly expressed in emerging floral meristems. Overexpression of DOSOC1 in wild-type Arabidopsis plants resulted in early flowering, which was coupled with the up-regulation of two other flowering promoters, AGAMOUS-LIKE 24 and LEAFY. In addition, overexpression of DOSOC1 was able partially to complement the late-flowering phenotype of Arabidopsis soc1-2 loss-of-function mutants. Furthermore, we successfully created seven 35S:DOSOC1 transgenic Dendrobium orchid lines, which consistently exhibited earlier flowering than wild-type orchids. Our results suggest that SOC1-like genes play an evolutionarily conserved role in promoting flowering in the Orchidaceae family, and that DOSOC1 isolated from Dendrobium Chao Praya Smile could serve as an important target for genetic manipulation of flowering time in orchids.

  17. IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.

    Science.gov (United States)

    Wong, Aaron K; Krishnan, Arjun; Yao, Victoria; Tadych, Alicja; Troyanskaya, Olga G

    2015-07-01

    IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  18. Following a drop of water from the cloud, throughout the sewer system, into the receiving water - Model predictive control of integrated sewer-wastewater treatment systems

    DEFF Research Database (Denmark)

    Mikkelsen, Peter Steen; Vezzaro, Luca; Sharma, Anitha Kumari

    This article presents selected examples of model-based prediction and control of integrated sewer-wastewater treatment systems, developed within the framework of the Storm- and Wastewater Informatics project (SWI). By exploiting all the available on-line information (e.g. radar based rainfall...... of pollutants discharged from treatment plants, etc.). The tools developed in the SWI project include (but are not limited to (i) rainfall nowcasting based on radar measurements, (ii) probabilistic flow forecasting based on data assimilation and stochastic models, (iii) prediction and optimization of wet......-weather performance of wastewater treatment plants, and (iv) integrated control of the different elements of the integrated wastewater systems. Full-scale testing of these tools in different catchment located in Denmark ensure that the developed tools can represent an important step forwards for on-line operation...

  19. A comparative gene analysis with rice identified orthologous group II HKT genes and their association with Na(+) concentration in bread wheat.

    Science.gov (United States)

    Ariyarathna, H A Chandima K; Oldach, Klaus H; Francki, Michael G

    2016-01-19

    Although the HKT transporter genes ascertain some of the key determinants of crop salt tolerance mechanisms, the diversity and functional role of group II HKT genes are not clearly understood in bread wheat. The advanced knowledge on rice HKT and whole genome sequence was, therefore, used in comparative gene analysis to identify orthologous wheat group II HKT genes and their role in trait variation under different saline environments. The four group II HKTs in rice identified two orthologous gene families from bread wheat, including the known TaHKT2;1 gene family and a new distinctly different gene family designated as TaHKT2;2. A single copy of TaHKT2;2 was found on each homeologous chromosome arm 7AL, 7BL and 7DL and each gene was expressed in leaf blade, sheath and root tissues under non-stressed and at 200 mM salt stressed conditions. The proteins encoded by genes of the TaHKT2;2 family revealed more than 93% amino acid sequence identity but ≤52% amino acid identity compared to the proteins encoded by TaHKT2;1 family. Specifically, variations in known critical domains predicted functional differences between the two protein families. Similar to orthologous rice genes on chromosome 6L, TaHKT2;1 and TaHKT2;2 genes were located approximately 3 kb apart on wheat chromosomes 7AL, 7BL and 7DL, forming a static syntenic block in the two species. The chromosomal region on 7AL containing TaHKT2;1 7AL-1 co-located with QTL for shoot Na(+) concentration and yield in some saline environments. The differences in copy number, genes sequences and encoded proteins between TaHKT2;2 homeologous genes and other group II HKT gene families within and across species likely reflect functional diversity for ion selectivity and transport in plants. Evidence indicated that neither TaHKT2;2 nor TaHKT2;1 were associated with primary root Na(+) uptake but TaHKT2;1 may be associated with trait variation for Na(+) exclusion and yield in some but not all saline environments.

  20. Ortholog Alleles at Xa3/Xa26 Locus Confer Conserved Race-Specific Resistance against Xanthomonas oryzae in Rice

    Institute of Scientific and Technical Information of China (English)

    Hong-Jing Li; Xiang-Hua Li; Jing-Hua Xiao; Rod A. Wing; Shi-Ping Wang

    2012-01-01

    The rice disease resistance (R) gene Xa3/Xa26 (having also been named Xa3 and Xa26) against Xanthomonas oryzae pv.oryzae (Xoo),which causes bacterial blight disease,belongs to a multiple gene family clustered in chromosome 11 and is from an AA genome rice cultivar (Oryza sativa L.).This family encodes leucine-rich repeat (LRR) receptor kinasetype proteins.Here,we show that the orthologs (alleles) of Xa3/Xa26,Xa3/Xa26-2,and Xa3/Xa26-3,from wild Oryza species O.officinalis (CC genome) and O.minuta (BBCC genome),respectively,were also R genes against Xoo.Xa3/Xa26-2 and Xa3/Xa26-3 conferred resistance to 16 of the 18 Xoo strains examined.Comparative sequence analysis of the Xa3/Xa26 families in the two wild Oryza species showed that Xa3/Xa26-3 appeared to have originated from the CC genome of O.minuta.The predicted proteins encoded by Xa3/Xa26,Xa3/Xa26-2,and Xa3/Xa26-3 share 91-99% sequence identity and 94-99% sequence similarity.Transgenic plants carrying a single copy of Xa3/Xa26,Xa3/Xa26-2,or Xa3/Xa26-3,in the same genetic background,showed a similar resistance spectrum to a set of Xoo strains,although plants carrying Xa3/Xa26-2 or Xa3/Xa26-3 showed lower resistance levels than the plants carrying Xa3/Xa26.These results suggest that the Xa3/Xa26 locus predates the speciation of A and C genome,which is approximately 7.5 million years ago.Thus,the resistance specificity of this locus has been conserved for a long time.

  1. On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report.

    Directory of Open Access Journals (Sweden)

    Paul D Thomas

    Full Text Available A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011 has proposed a metric for the "functional similarity" between two genes that uses only the Gene Ontology (GO annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the "ortholog conjecture" (or, more properly, the "ortholog functional conservation hypothesis". First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1 that GO annotations are often incomplete, potentially in a biased manner, and subject to an "open world assumption" (absence of an annotation does not imply absence of a function, and 2 that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the

  2. An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

    Science.gov (United States)

    Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

    2016-12-01

    Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus

  3. Satellite-guided hydro-economic analysis for integrated management and prediction of the impact of droughts on agricultural regions

    Science.gov (United States)

    Maneta, M. P.; Howitt, R.; Kimball, J. S.

    2013-12-01

    Agricultural activity can exacerbate or buffer the impact of climate variability, especially droughts, on the hydrologic and socioeconomic conditions of rural areas. Potential negative regional impacts of droughts include impoverishment of agricultural regions, deterioration or overuse of water resources, risk of monoculture, and regional dependence on external food markets. Policies that encourage adequate management practices in the face of adverse climatic events are critical to preserve rural livelihoods and to ensure a sustainable future for agriculture. Diagnosing and managing drought effects on agricultural production, on the social and natural environment, and on limited water resources, is highly complex and interdisciplinary. The challenges that decision-makers face to mitigate the impact of water shortage are social, agronomic, economic and environmental in nature and therefore must be approached from an integrated multidisciplinary point of view. Existing observation technologies, in conjunction with models and assimilation methods open the opportunity for novel interdisciplinary analysis tools to support policy and decision making. We present an integrated modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, management decisions and socioeconomic policy. The core of this framework is a hydroeconomic model of agricultural production that assimilates remote sensing inputs to quantify the amount of land, water, fertilizer and labor farmers allocate for each crop they choose to grow on a seasonal basis in response to changing climatic conditions, including drought. A regional hydroclimatologic model provides biophysical constraints to an economic model of agricultural production based on a class of models referred to as positive mathematical programming (PMP

  4. Development and bin mapping of a Rosaceae Conserved Ortholog Set (COS of markers

    Directory of Open Access Journals (Sweden)

    Kozik Alex

    2009-01-01

    Full Text Available Abstract Background Detailed comparative genome analyses within the economically important Rosaceae family have not been conducted. This is largely due to the lack of conserved gene-based molecular markers that are transferable among the important crop genera within the family [e.g. Malus (apple, Fragaria (strawberry, and Prunus (peach, cherry, apricot and almond]. The lack of molecular markers and comparative whole genome sequence analysis for this family severely hampers crop improvement efforts as well as QTL confirmation and validation studies. Results We identified a set of 3,818 rosaceaous unigenes comprised of two or more ESTs that correspond to single copy Arabidopsis genes. From this Rosaceae Conserved Orthologous Set (RosCOS, 1039 were selected from which 857 were used for the development of intron-flanking primers and allele amplification. This led to successful amplification and subsequent mapping of 613 RosCOS onto the Prunus TxE reference map resulting in a genome-wide coverage of 0.67 to 1.06 gene-based markers per cM per linkage group. Furthermore, the RosCOS primers showed amplification success rates from 23 to 100% across the family indicating that a substantial part of the RosCOS primers can be directly employed in other less studied rosaceaous crops. Comparisons of the genetic map positions of the RosCOS with the physical locations of the orthologs in the Populus trichocarpa genome identified regions of colinearity between the genomes of Prunus-Rosaceae and Populus-Salicaceae. Conclusion Conserved orthologous genes are extremely useful for the analysis of genome evolution among closely and distantly related species. The results presented in this study demonstrate the considerable potential of the mapped Prunus RosCOS for genome-wide marker employment and comparative whole genome studies within the Rosaceae family. Moreover, these markers will also function as useful anchor points for the genome sequencing efforts currently

  5. Mutations that Allow SIR2 Orthologs to Function in a NAD+-Depleted Environment.

    Science.gov (United States)

    Ondracek, Caitlin R; Frappier, Vincent; Ringel, Alison E; Wolberger, Cynthia; Guarente, Leonard

    2017-03-07

    Sirtuin enzymes depend on NAD + to catalyze protein deacetylation. Therefore, the lowering of NAD + during aging leads to decreased sirtuin activity and may speed up aging processes in laboratory animals and humans. In this study, we used a genetic screen to identify two mutations in the catalytic domain of yeast Sir2 that allow the enzyme to function in an NAD + -depleted environment. These mutant enzymes give rise to a significant increase of yeast replicative lifespan and increase deacetylation by the Sir2 ortholog, SIRT1, in mammalian cells. Our data suggest that these mutations increase the stability of the conserved catalytic sirtuin domain, thereby increasing the catalytic efficiency of the mutant enzymes. Our approach to identifying sirtuin mutants that permit function in NAD + -limited environments may inform the design of small molecules that can maintain sirtuin activity in aging organisms. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  6. The XMAP215 Ortholog Alp14 Promotes Microtubule Nucleation in Fission Yeast.

    Science.gov (United States)

    Flor-Parra, Ignacio; Iglesias-Romero, Ana Belén; Chang, Fred

    2018-06-04

    The organization and number of microtubules (MTs) in a cell depend on the proper regulation of MT nucleation. Currently, the mechanism of nucleation is the most poorly understood aspect of MT dynamics. XMAP215/chTOG/Alp14/Stu2 proteins are MT polymerases that stimulate MT polymerization at MT plus ends by binding and releasing tubulin dimers. Although these proteins also localize to MT organizing centers and have nucleating activity in vitro, it is not yet clear whether these proteins participate in MT nucleation in vivo. Here, we demonstrate that in the fission yeast Schizosaccharomyces pombe, the XMAP215 ortholog Alp14 is critical for efficient MT nucleation in vivo. In multiple assays, loss of Alp14 function led to reduced nucleation rate and numbers of interphase MT bundles. Conversely, activation of Alp14 led to increased nucleation frequency. Alp14 associated with Mto1 and γ-tubulin complex components, and artificially targeting Alp14 to the γ-tubulin ring complexes (γ-TuRCs) stimulated nucleation. In imaging individual nucleation events, we found that Alp14 transiently associated with a γ-tubulin particle shortly before the appearance of a new MT. The transforming acidic coiled-coil (TACC) ortholog Alp7 mediated the localization of Alp14 at nucleation sites but not plus ends, and was required for efficient nucleation but not for MT polymerization. Our findings provide the strongest evidence to date that Alp14 serves as a critical MT nucleation factor in vivo. We suggest a model in which Alp14 associates with the γ-tubulin complex in an Alp7-dependent manner to facilitate the assembly or stabilization of the nascent MT. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. The fission yeast MTREC and EJC orthologs ensure the maturation of meiotic transcripts during meiosis.

    Science.gov (United States)

    Marayati, Bahjat Fadi; Hoskins, Victoria; Boger, Robert W; Tucker, James F; Fishman, Emily S; Bray, Andrew S; Zhang, Ke

    2016-09-01

    Meiosis is a highly regulated process by which genetic information is transmitted through sexual reproduction. It encompasses unique mechanisms that do not occur in vegetative cells, producing a distinct, well-regulated meiotic transcriptome. During vegetative growth, many meiotic genes are constitutively transcribed, but most of the resulting mRNAs are rapidly eliminated by the Mmi1-MTREC (Mtl1-Red1 core) complex. While Mmi1-MTREC targets premature meiotic RNAs for degradation by the nuclear 3'-5' exoribonuclease exosome during mitotic growth, its role in meiotic gene expression during meiosis is not known. Here, we report that Red5, an essential MTREC component, interacts with pFal1, an ortholog of eukaryotic translation initiation factor eIF4aIII in the fission yeast Schizosaccharomyces pombe In mammals, together with MAGO (Mnh1), Rnps1, and Y14, elF4AIII (pFal1) forms the core of the exon junction complex (EJC), which is essential for transcriptional surveillance and localization of mature mRNAs. In fission yeast, two EJC orthologs, pFal1 and Mnh1, are functionally connected with MTREC, specifically in the process of meiotic gene expression during meiosis. Although pFal1 interacts with Mnh1, Y14, and Rnps1, its association with Mnh1 is not disrupted upon loss of Y14 or Rnps1. Mutations of Red1, Red5, pFal1, or Mnh1 produce severe meiotic defects; the abundance of meiotic transcripts during meiosis decreases; and mRNA maturation processes such as splicing are impaired. Since studying meiosis in mammalian germline cells is difficult, our findings in fission yeast may help to define the general mechanisms involved in accurate meiotic gene expression in higher eukaryotes. © 2016 Marayati et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  8. Linking the potato genome to the conserved ortholog set (COS) markers

    Science.gov (United States)

    2013-01-01

    Background Conserved ortholog set (COS) markers are an important functional genomics resource that has greatly improved orthology detection in Asterid species. A comprehensive list of these markers is available at Sol Genomics Network (http://solgenomics.net/) and many of these have been placed on the genetic maps of a number of solanaceous species. Results We amplified over 300 COS markers from eight potato accessions involving two diploid landraces of Solanum tuberosum Andigenum group (formerly classified as S. goniocalyx, S. phureja), and a dihaploid clone derived from a modern tetraploid cultivar of S. tuberosum and the wild species S. berthaultii, S. chomatophilum, and S. paucissectum. By BLASTn (Basic Local Alignment Search Tool of the NCBI, National Center for Biotechnology Information) algorithm we mapped the DNA sequences of these markers into the potato genome sequence. Additionally, we mapped a subset of these markers genetically in potato and present a comparison between the physical and genetic locations of these markers in potato and in comparison with the genetic location in tomato. We found that most of the COS markers are single-copy in the reference genome of potato and that the genetic location in tomato and physical location in potato sequence are mostly in agreement. However, we did find some COS markers that are present in multiple copies and those that map in unexpected locations. Sequence comparisons between species show that some of these markers may be paralogs. Conclusions The sequence-based physical map becomes helpful in identification of markers for traits of interest thereby reducing the number of markers to be tested for applications like marker assisted selection, diversity, and phylogenetic studies. PMID:23758607

  9. The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator

    Energy Technology Data Exchange (ETDEWEB)

    Grant, Claire, E-mail: claire.grant@astrazeneca.com [Drug Safety and Metabolism, AstraZeneca, Alderley Park, Macclesfield SK10 4TG (United Kingdom); Ewart, Lorna [Drug Safety and Metabolism, AstraZeneca, Da Vinci Building, Melbourn Science Park, Cambridge Road, Melbourn, Royston SG8 6HB (United Kingdom); Muthas, Daniel [Respiratory, Inflammation and Autoimmunity iMED, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal (Sweden); Deavall, Damian [Drug Safety and Metabolism, AstraZeneca, Alderley Park, Macclesfield SK10 4TG (United Kingdom); Smith, Simon A. [Oncology Translational Medicine Unit, Early Clinical Development, AstraZeneca, Da Vinci Building, Melbourn Science Park, Melbourn, Royston SG8 6HB (United Kingdom); Clack, Glen [Translational Medicine Unit, Early Clinical Development, AstraZeneca, Alderley Park, Macclesfield SK10 4TG (United Kingdom); Newham, Pete [Drug Safety and Metabolism, AstraZeneca, Darwin Building, Cambridge Science Park, Milton Road, Cambridge CB4 0WG (United Kingdom)

    2016-04-01

    Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility of integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive “pica” behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. - Highlights: • Integrated pre-clinical data can be used to predict clinical nausea and vomiting. • Data integrated from standard toxicology studies is sufficient to make a prediction. • The use of the nausea algorithm developed by Parkinson (2012) aids the prediction. • Additional pre-clinical studies can be used

  10. From a single encapsulated detector to the spectrometer for INTEGRAL satellite: predicting the peak-to-total ratio at high gamma-energies

    OpenAIRE

    Kshetri, Ritesh

    2012-01-01

    In two recent papers (R. Kshetri, JINST 2012 7 P04008; ibid., P07006), a probabilistic formalism was introduced to predict the response of encapsulated type composite germanium detectors like the SPI (spectrometer for INTEGRAL satellite). Predictions for the peak-to-total and peak-to-background ratios are given at 1.3 MeV for the addback mode of operation. The application of the formalism to clover germanium detector is discussed in two separate papers (R. Kshetri, JINST 2012 7 P07008; ibid.,...

  11. Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves

    Science.gov (United States)

    Zhu, Linqi; Zhang, Chong; Zhang, Chaomo; Wei, Yang; Zhou, Xueqing; Cheng, Yuan; Huang, Yuyang; Zhang, Le

    2018-06-01

    There is increasing interest in shale gas reservoirs due to their abundant reserves. As a key evaluation criterion, the total organic carbon content (TOC) of the reservoirs can reflect its hydrocarbon generation potential. The existing TOC calculation model is not very accurate and there is still the possibility for improvement. In this paper, an integrated hybrid neural network (IHNN) model is proposed for predicting the TOC. This is based on the fact that the TOC information on the low TOC reservoir, where the TOC is easy to evaluate, comes from a prediction problem, which is the inherent problem of the existing algorithm. By comparing the prediction models established in 132 rock samples in the shale gas reservoir within the Jiaoshiba area, it can be seen that the accuracy of the proposed IHNN model is much higher than that of the other prediction models. The mean square error of the samples, which were not joined to the established models, was reduced from 0.586 to 0.442. The results show that TOC prediction is easier after logging prediction has been improved. Furthermore, this paper puts forward the next research direction of the prediction model. The IHNN algorithm can help evaluate the TOC of a shale gas reservoir.

  12. 6-Pyruvoyltetrahydropterin synthase orthologs of either a single or dual domain structure are responsible for tetrahydrobiopterin synthesis in bacteria.

    Science.gov (United States)

    Kong, Jin Sun; Kang, Ji-Youn; Kim, Hye Lim; Kwon, O-Seob; Lee, Kon Ho; Park, Young Shik

    2006-09-04

    6-Pyruvoyltetrahydropterin synthase (PTPS) catalyzes the second step of tetrahydrobiopterin (BH4) synthesis. We previously identified PTPS orthologs (bPTPS-Is) in bacteria which do not produce BH4. In this study we disrupted the gene encoding bPTPS-I in Synechococcus sp. PCC 7942, which produces BH4-glucoside. The mutant was normal in BH4-glucoside production, demonstrating that bPTPS-I does not participate in BH4 synthesis in vivo and bringing us a new PTPS ortholog (bPTPS-II) of a bimodular polypeptide. The recombinant Synechococcus bPTPS-II was assayed in vitro to show PTPS activity higher than human enzyme. Further computational analysis revealed the presence of mono and bimodular bPTPS-II orthologs mostly in green sulfur bacteria and cyanobacteria, respectively, which are well known for BH4-glycoside production. In summary we found new bacterial PTPS orthologs, having either a single or dual domain structure and being responsible for BH4 synthesis in vivo, thereby disclosing all the bacterial PTPS homologs.

  13. Mycoplasma hyopneumoniae and Mycoplasma flocculare differential domains from orthologous surface proteins induce distinct cellular immune responses in mice.

    Science.gov (United States)

    Leal, Fernanda Munhoz Dos Anjos; Virginio, Veridiana Gomes; Martello, Carolina Lumertz; Paes, Jéssica Andrade; Borges, Thiago J; Jaeger, Natália; Bonorino, Cristina; Ferreira, Henrique Bunselmeyer

    2016-07-15

    Mycoplasma hyopneumoniae and Mycoplasma flocculare are two genetically close species found in the swine respiratory tract. Despite their similarities, while M. hyopneumoniae is the causative agent of porcine enzootic pneumonia, M. flocculare is a commensal bacterium. Genomic and transcriptional comparative analyses so far failed to explain the difference in pathogenicity between these two species. We then hypothesized that such difference might be, at least in part, explained by amino acid sequence and immunological or functional differences between ortholog surface proteins. In line with that, it was verified that approximately 85% of the ortholog surface proteins from M. hyopneumoniae 7448 and M. flocculare present one or more differential domains. To experimentally assess possible immunological implications of this kind of difference, the extracellular differential domains from one pair of orthologous surface proteins (MHP7448_0612, from M. hyopneumoniae, and MF_00357, from M. flocculare) were expressed in E. coli and used to immunize mice. The recombinant polypeptides (rMHP61267-169 and rMF35767-196, respectively) induced distinct cellular immune responses. While, rMHP61267-169 induced both Th1 and Th2 responses, rMF35767-196 induced just an early pro-inflammatory response. These results indicate that immunological properties determined by differential domains in orthologous surface protein might play a role in pathogenicity, contributing to elicit specific and differential immune responses against each species. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models

    Directory of Open Access Journals (Sweden)

    LeDonne Norman C

    2011-02-01

    Full Text Available Abstract Background Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI curves via the SALI curve integral (SCI as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists. Results The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK in vitro efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from SimulationsPlus and AstraZeneca. Conclusion The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.

  15. Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models.

    Science.gov (United States)

    Ledonne, Norman C; Rissolo, Kevin; Bulgarelli, James; Tini, Leonard

    2011-02-07

    Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI) curves via the SALI curve integral (SCI) as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists. The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK in vitro efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from SimulationsPlus and AstraZeneca. The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.

  16. Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

    Science.gov (United States)

    Galpert, Deborah; Fernández, Alberto; Herrera, Francisco; Antunes, Agostinho; Molina-Ruiz, Reinaldo; Agüero-Chapin, Guillermin

    2018-05-03

    The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were

  17. Towards understanding the first genome sequence of a crenarchaeon by genome annotation using clusters of orthologous groups of proteins (COGs).

    Science.gov (United States)

    Natale, D A; Shankavaram, U T; Galperin, M Y; Wolf, Y I; Aravind, L; Koonin, E V

    2000-01-01

    Standard archival sequence databases have not been designed as tools for genome annotation and are far from being optimal for this purpose. We used the database of Clusters of Orthologous Groups of proteins (COGs) to reannotate the genomes of two archaea, Aeropyrum pernix, the first member of the Crenarchaea to be sequenced, and Pyrococcus abyssi. A. pernix and P. abyssi proteins were assigned to COGs using the COGNITOR program; the results were verified on a case-by-case basis and augmented by additional database searches using the PSI-BLAST and TBLASTN programs. Functions were predicted for over 300 proteins from A. pernix, which could not be assigned a function using conventional methods with a conservative sequence similarity threshold, an approximately 50% increase compared to the original annotation. A. pernix shares most of the conserved core of proteins that were previously identified in the Euryarchaeota. Cluster analysis or distance matrix tree construction based on the co-occurrence of genomes in COGs showed that A. pernix forms a distinct group within the archaea, although grouping with the two species of Pyrococci, indicative of similar repertoires of conserved genes, was observed. No indication of a specific relationship between Crenarchaeota and eukaryotes was obtained in these analyses. Several proteins that are conserved in Euryarchaeota and most bacteria are unexpectedly missing in A. pernix, including the entire set of de novo purine biosynthesis enzymes, the GTPase FtsZ (a key component of the bacterial and euryarchaeal cell-division machinery), and the tRNA-specific pseudouridine synthase, previously considered universal. A. pernix is represented in 48 COGs that do not contain any euryarchaeal members. Many of these proteins are TCA cycle and electron transport chain enzymes, reflecting the aerobic lifestyle of A. pernix. Special-purpose databases organized on the basis of phylogenetic analysis and carefully curated with respect to known and

  18. IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction

    Directory of Open Access Journals (Sweden)

    Kiran Sree Pokkuluri

    2014-01-01

    Full Text Available Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000. The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata and MCC (modified clonal classifier to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992 datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006 dataset and nonpromoters from EID (Saxonov et al., 2000 and UTRdb (Pesole et al., 2002 datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

  19. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Denglong [Fuli School of Food Equipment Engineering and Science, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); Zhang, Zaoxiao, E-mail: zhangzx@mail.xjtu.edu.cn [State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); School of Chemical Engineering and Technology, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China)

    2016-07-05

    Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

  20. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

    International Nuclear Information System (INIS)

    Ma, Denglong; Zhang, Zaoxiao

    2016-01-01

    Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

  1. Protein function prediction involved on radio-resistant bacteria

    International Nuclear Information System (INIS)

    Mezhoud, Karim; Mankai, Houda; Sghaier, Haitham; Barkallah, Insaf

    2009-01-01

    Previously, we identified 58 proteins under positive selection in ionizing-radiation-resistant bacteria (IRRB) but absent in all ionizing-radiation-sensitive bacteria (IRSB). These are good reasons to believe these 58 proteins with their interactions with other proteins (interactomes) are a part of the answer to the question as to how IRRB resist to radiation, because our knowledge of interactomes of positively selected orphan proteins in IRRB might allow us to define cellular pathways important to ionizing-radiation resistance. Using the Database of Interacting Proteins and the PSIbase, we have predicted interactions of orthologs of the 58 proteins under positive selection in IRRB but absent in all IRSB. We used integrate experimental data sets with molecular interaction networks and protein structure prediction from databases. Among these, 18 proteins with their interactomes were identified in Deinococcus radiodurans R1. DNA checkpoint and repair, kinases pathways, energetic and nucleotide metabolisms were the important biological process that found. We predicted the interactomes of 58 proteins under positive selection in IRRB. It is hoped our data will provide new clues as to the cellular pathways that are important for ionizing-radiation resistance. We have identified news proteins involved on DNA management which were not previously mentioned. It is an important input in addition to protein that studied. It does still work to deepen our study on these new proteins

  2. Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows.

    Directory of Open Access Journals (Sweden)

    Nina Melzer

    Full Text Available In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach. To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317 SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype.

  3. A predictive index of biotic integrity model for aquatic-vertebrate assemblages of Western U.S. streams

    Science.gov (United States)

    Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder ri...

  4. Psychomotor Battery Approaches to Performance Prediction and Evaluation in Hyperbaric, Thermal and Vibratory Environments: Annotated Bibliographies and Integrative Review

    Science.gov (United States)

    1980-10-01

    W77-Mar78 and Vibratory Environments: Annotated Biblia - 4.-EFRIGOO EOT*_1 graphies and Integrative Review. I. CONTRACT OR GRANT NUMSER(a) David J...Papers In the third phase of the effort, the final version of the three speciai-environrneni performance battery bibliographies was corriiled and the...performance at much lower pressu. (e.g. 3 to 4 ATA when nitrogen is involved). The following sections will integrate the available liter - ature on the effects

  5. SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

    DEFF Research Database (Denmark)

    Guisan, Antoine; Rahbek, Carsten

    2011-01-01

    Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts...

  6. THE INTEGRATED USE OF COMPUTATIONAL CHEMISTRY, SCANNING PROBE MICROSCOPY, AND VIRTUAL REALITY TO PREDICT THE CHEMICAL REACTIVITY OF ENVIRONMENTAL SURFACES

    Science.gov (United States)

    In the last decade three new techniques scanning probe microscopy (SPM), virtual reality (YR) and computational chemistry ave emerged with the combined capability of a priori predicting the chemically reactivity of environmental surfaces. Computational chemistry provides the cap...

  7. A measurement-based method for predicting margins and uncertainties for unprotected accidents in the Integral Fast Reactor concept

    International Nuclear Information System (INIS)

    Vilim, R.B.

    1990-01-01

    A measurement-based method for predicting the response of an LMR core to unprotected accidents has been developed. The method processes plant measurements taken at normal operation to generate a stochastic model for the core dynamics. This model can be used to predict three sigma confidence intervals for the core temperature and power response. Preliminary numerical simulations performed for EBR-2 appear promising. 6 refs., 2 figs

  8. Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

    Science.gov (United States)

    Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing

    2017-02-01

    UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.

  9. The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

    Science.gov (United States)

    Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

    2014-05-01

    Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

  10. Identification of novel human damage response proteins targeted through yeast orthology.

    Directory of Open Access Journals (Sweden)

    J Peter Svensson

    Full Text Available Studies in Saccharomyces cerevisiae show that many proteins influence cellular survival upon exposure to DNA damaging agents. We hypothesized that human orthologs of these S. cerevisiae proteins would also be required for cellular survival after treatment with DNA damaging agents. For this purpose, human homologs of S. cerevisiae proteins were identified and mapped onto the human protein-protein interaction network. The resulting human network was highly modular and a series of selection rules were implemented to identify 45 candidates for human toxicity-modulating proteins. The corresponding transcripts were targeted by RNA interference in human cells. The cell lines with depleted target expression were challenged with three DNA damaging agents: the alkylating agents MMS and 4-NQO, and the oxidizing agent t-BuOOH. A comparison of the survival revealed that the majority (74% of proteins conferred either sensitivity or resistance. The identified human toxicity-modulating proteins represent a variety of biological functions: autophagy, chromatin modifications, RNA and protein metabolism, and telomere maintenance. Further studies revealed that MMS-induced autophagy increase the survival of cells treated with DNA damaging agents. In summary, we show that damage recovery proteins in humans can be identified through homology to S. cerevisiae and that many of the same pathways are represented among the toxicity modulators.

  11. An ortholog of LEAFY in Jatropha curcas regulates flowering time and floral organ development.

    Science.gov (United States)

    Tang, Mingyong; Tao, Yan-Bin; Fu, Qiantang; Song, Yaling; Niu, Longjian; Xu, Zeng-Fu

    2016-11-21

    Jatropha curcas seeds are an excellent biofuel feedstock, but seed yields of Jatropha are limited by its poor flowering and fruiting ability. Thus, identifying genes controlling flowering is critical for genetic improvement of seed yield. We isolated the JcLFY, a Jatropha ortholog of Arabidopsis thaliana LEAFY (LFY), and identified JcLFY function by overexpressing it in Arabidopsis and Jatropha. JcLFY is expressed in Jatropha inflorescence buds, flower buds, and carpels, with highest expression in the early developmental stage of flower buds. JcLFY overexpression induced early flowering, solitary flowers, and terminal flowers in Arabidopsis, and also rescued the delayed flowering phenotype of lfy-15, a LFY loss-of-function Arabidopsis mutant. Microarray and qPCR analysis revealed several flower identity and flower organ development genes were upregulated in JcLFY-overexpressing Arabidopsis. JcLFY overexpression in Jatropha also induced early flowering. Significant changes in inflorescence structure, floral organs, and fruit shape occurred in JcLFY co-suppressed plants in which expression of several flower identity and floral organ development genes were changed. This suggests JcLFY is involved in regulating flower identity, floral organ patterns, and fruit shape, although JcLFY function in Jatropha floral meristem determination is not as strong as that of Arabidopsis.

  12. Inference of gene-phenotype associations via protein-protein interaction and orthology.

    Directory of Open Access Journals (Sweden)

    Panwen Wang

    Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

  13. Genome Wide Identification of Orthologous ZIP Genes Associated with Zinc and Iron Translocation in Setaria italica.

    Science.gov (United States)

    Alagarasan, Ganesh; Dubey, Mahima; Aswathy, Kumar S; Chandel, Girish

    2017-01-01

    Genes in the ZIP family encode transcripts to store and transport bivalent metal micronutrient, particularly iron (Fe) and or zinc (Zn). These transcripts are important for a variety of functions involved in the developmental and physiological processes in many plant species, including most, if not all, Poaceae plant species and the model species Arabidopsis. Here, we present the report of a genome wide investigation of orthologous ZIP genes in Setaria italica and the identification of 7 single copy genes. RT-PCR shows 4 of them could be used to increase the bio-availability of zinc and iron content in grains. Of 36 ZIP members, 25 genes have traces of signal peptide based sub-cellular localization, as compared to those of plant species studied previously, yet translocation of ions remains unclear. In silico analysis of gene structure and protein nature suggests that these two were preeminent in shaping the functional diversity of the ZIP gene family in S. italica . NAC, bZIP and bHLH are the predominant Fe and Zn responsive transcription factors present in SiZIP genes. Together, our results provide new insights into the signal peptide based/independent iron and zinc translocation in the plant system and allowed identification of ZIP genes that may be involved in the zinc and iron absorption from the soil, and thus transporting it to the cereal grain underlying high micronutrient accumulation.

  14. Genome Wide Identification of Orthologous ZIP Genes Associated with Zinc and Iron Translocation in Setaria italica

    Directory of Open Access Journals (Sweden)

    Ganesh Alagarasan

    2017-05-01

    Full Text Available Genes in the ZIP family encode transcripts to store and transport bivalent metal micronutrient, particularly iron (Fe and or zinc (Zn. These transcripts are important for a variety of functions involved in the developmental and physiological processes in many plant species, including most, if not all, Poaceae plant species and the model species Arabidopsis. Here, we present the report of a genome wide investigation of orthologous ZIP genes in Setaria italica and the identification of 7 single copy genes. RT-PCR shows 4 of them could be used to increase the bio-availability of zinc and iron content in grains. Of 36 ZIP members, 25 genes have traces of signal peptide based sub-cellular localization, as compared to those of plant species studied previously, yet translocation of ions remains unclear. In silico analysis of gene structure and protein nature suggests that these two were preeminent in shaping the functional diversity of the ZIP gene family in S. italica. NAC, bZIP and bHLH are the predominant Fe and Zn responsive transcription factors present in SiZIP genes. Together, our results provide new insights into the signal peptide based/independent iron and zinc translocation in the plant system and allowed identification of ZIP genes that may be involved in the zinc and iron absorption from the soil, and thus transporting it to the cereal grain underlying high micronutrient accumulation.

  15. Identification of putative orthologous genes for the phylogenetic reconstruction of temperate woody bamboos (Poaceae: Bambusoideae).

    Science.gov (United States)

    Zhang, Li-Na; Zhang, Xian-Zhi; Zhang, Yu-Xiao; Zeng, Chun-Xia; Ma, Peng-Fei; Zhao, Lei; Guo, Zhen-Hua; Li, De-Zhu

    2014-09-01

    The temperate woody bamboos (Arundinarieae) are highly diverse in morphology but lack a substantial amount of genetic variation. The taxonomy of this lineage is intractable, and the relationships within the tribe have not been well resolved. Recent studies indicated that this tribe could have a complex evolutionary history. Although phylogenetic studies of the tribe have been carried out, most of these phylogenetic reconstructions were based on plastid data, which provide lower phylogenetic resolution compared with nuclear data. In this study, we intended to identify a set of desirable nuclear genes for resolving the phylogeny of the temperate woody bamboos. Using two different methodologies, we identified 209 and 916 genes, respectively, as putative single copy orthologous genes. A total of 112 genes was successfully amplified and sequenced by next-generation sequencing technologies in five species sampled from the tribe. As most of the genes exhibited intra-individual allele heterozygotes, we investigated phylogenetic utility by reconstructing the phylogeny based on individual genes. Discordance among gene trees was observed and, to resolve the conflict, we performed a range of analyses using BUCKy and HybTree. While caution should be taken when inferring a phylogeny from multiple conflicting genes, our analysis indicated that 74 of the 112 investigated genes are potential markers for resolving the phylogeny of the temperate woody bamboos. © 2014 John Wiley & Sons Ltd.

  16. Bloom syndrome ortholog HIM-6 maintains genomic stability in C. elegans.

    Science.gov (United States)

    Grabowski, Melissa M; Svrzikapa, Nenad; Tissenbaum, Heidi A

    2005-12-01

    Bloom syndrome is caused by mutation of the Bloom helicase (BLM), a member of the RecQ helicase family. Loss of BLM function results in genomic instability that causes a high incidence of cancer. It has been demonstrated that BLM is important for maintaining genomic stability by playing a role in DNA recombination and repair; however, the exact function of BLM is not clearly understood. To determine the mechanism by which BLM controls genomic stability in vivo, we examined the phenotypes caused by mutation of the C. elegans BLM helicase ortholog, HIM-6. We find that the loss of HIM-6 leads to genomic instability as evidenced by an increased number of genomic insertions and deletions, which results in visible random mutant phenotypes. In addition to the mutator phenotype, him-6 mutants have a low brood size, a high incidence of males, a shortened life span, and an increased amount of germ line apoptosis. Upon exposure to high temperature, him-6 mutants that are serially passed become sterile demonstrating a mortal germ line phenotype. Our data suggest a model in which loss of HIM-6 results in genomic instability due to an increased number of DNA lesions, which either cannot be repaired and/or are introduced by low fidelity recombination events. The increased level of genomic instability that leads to him-6(ok412) mutants having a shortened life span.

  17. ATX-2, the C. elegans Ortholog of Human Ataxin-2, Regulates Centrosome Size and Microtubule Dynamics.

    Directory of Open Access Journals (Sweden)

    Michael D Stubenvoll

    2016-09-01

    Full Text Available Centrosomes are critical sites for orchestrating microtubule dynamics, and exhibit dynamic changes in size during the cell cycle. As cells progress to mitosis, centrosomes recruit more microtubules (MT to form mitotic bipolar spindles that ensure proper chromosome segregation. We report a new role for ATX-2, a C. elegans ortholog of Human Ataxin-2, in regulating centrosome size and MT dynamics. ATX-2, an RNA-binding protein, forms a complex with SZY-20 in an RNA-independent fashion. Depleting ATX-2 results in embryonic lethality and cytokinesis failure, and restores centrosome duplication to zyg-1 mutants. In this pathway, SZY-20 promotes ATX-2 abundance, which inversely correlates with centrosome size. Centrosomes depleted of ATX-2 exhibit elevated levels of centrosome factors (ZYG-1, SPD-5, γ-Tubulin, increasing MT nucleating activity but impeding MT growth. We show that ATX-2 influences MT behavior through γ-Tubulin at the centrosome. Our data suggest that RNA-binding proteins play an active role in controlling MT dynamics and provide insight into the control of proper centrosome size and MT dynamics.

  18. PSP: rapid identification of orthologous coding genes under positive selection across multiple closely related prokaryotic genomes.

    Science.gov (United States)

    Su, Fei; Ou, Hong-Yu; Tao, Fei; Tang, Hongzhi; Xu, Ping

    2013-12-27

    With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure. Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/. PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.

  19. Integration of renewable and conventional power. Intraday optimization, pooling and predictive target value generation; Intraday-Optimierung, Pooling and vorausschauende Zielsollwertfuehrung. Integration von erneuerbaren und konventionellen Energien

    Energy Technology Data Exchange (ETDEWEB)

    Franke, Ruediger; Kautsch, Stephan; Blaumann, Marcel; Vogelbacher, Lothar [ABB AG, Mannheim (Germany)

    2013-10-01

    Facing increasing use of fluctuating renewable energies, the traditional unit commitment on the previous day and the use of balancing energy to account for deviations on the current day is running into limitations. Intraday optimization adapts plant schedules on the current day to new situations. This leads to frequently changing plant schedules, up to one change every 15 minutes and requires a lot of flexibility from conventional power plants. Pooling reduces the complexity of the overall system by introducing hierarchies. The predictive generation of target set points considers multiple subsequent changes of the schedule, in order to obtain a plant operation that matches at discrete accounting points and provides a smooth operation in between. The paper investigates the realization of the new techniques with online optimization. (orig.)

  20. Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study

    Science.gov (United States)

    Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

    2012-01-01

    Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2. Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most

  1. Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study.

    Science.gov (United States)

    Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

    2012-03-27

    Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1-7.2. Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1-7.2 is most promising.

  2. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

    Science.gov (United States)

    Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

    2018-08-01

    Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Identification of genes involved in a water stress response in timothy and mapping of orthologous loci in perennial ryegrass

    DEFF Research Database (Denmark)

    Jonavičienė, Kristina; Studer, Bruno; Asp, Torben

    2012-01-01

    In order to characterize the response of selected grasses to water stress, relative water content (RWC) in leaves and quantum efficiency of photosystem 2 (Fv/Fm) were measured in Phleum pratense L., P. bertolonii DC. and P. phleoides H. Karst. during 6 d of water stress. The results indicated...... differential responses to water stress among the three Phleum species with higher water deficit sensitivity of P. p