178 research outputs found

    Correction: Comparative genomic profiling of Dutch clinical Bordetella pertussis isolates using DNA microarrays: identification of genes absent from epidemic strains

    Get PDF
    <p><b>Copyright information:</b></p><p>Taken from "Comparative genomic profiling of Dutch clinical isolates using DNA microarrays: Identification of genes absent from epidemic strains"</p><p>http://www.biomedcentral.com/1471-2164/9/311</p><p>BMC Genomics 2008;9():311-311.</p><p>Published online 30 Jun 2008</p><p>PMCID:PMC2481270.</p><p></p

    On environment difficulty and discriminating power

    Full text link
    The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). Robotics competitions as benchmarks for ai research. The Knowledge Engineering Review, 26(01), 11–17.Andre, D., & Russell, S. J. (2002). State abstraction for programmable reinforcement learning agents. In Proceedings of the National Conference on Artificial Intelligence (pp. 119–125). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Antunes, L., Fortnow, L., van Melkebeek, D., & Vinodchandran, N. V. (2006). Computational depth: Concept and applications. Theoretical Computer Science, 354(3), 391–404. Foundations of Computation Theory (FCT 2003), 14th Symposium on Fundamentals of Computation Theory 2003.Arai, K., Kaminka, G. A., Frank, I., & Tanaka-Ishii, K. (2003). Performance competitions as research infrastructure: Large scale comparative studies of multi-agent teams. Autonomous Agents and Multi-Agent Systems, 7(1–2), 121–144.Ashcraft, M. H., Donley, R. D., Halas, M. A., & Vakali, M. (1992). Chapter 8 working memory, automaticity, and problem difficulty. In Jamie I.D. Campbell (Ed.), The nature and origins of mathematical skills, volume 91 of advances in psychology (pp. 301–329). North-Holland.Ay, N., Müller, M., & Szkola, A. (2010). Effective complexity and its relation to logical depth. IEEE Transactions on Information Theory, 56(9), 4593–4607.Barch, D. M., Braver, T. S., Nystrom, L. E., Forman, S. D., Noll, D. C., & Cohen, J. D. (1997). Dissociating working memory from task difficulty in human prefrontal cortex. Neuropsychologia, 35(10), 1373–1380.Bordini, R. H., Hübner, J. F., & Wooldridge, M. (2007). Programming multi-agent systems in AgentSpeak using Jason. London: Wiley. com.Boutilier, C., Reiter, R., Soutchanski, M., Thrun, S. et al. (2000). Decision-theoretic, high-level agent programming in the situation calculus. In Proceedings of the National Conference on Artificial Intelligence (pp. 355–362). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 38(2), 156–172.Chaitin, G. J. (1977). Algorithmic information theory. IBM Journal of Research and Development, 21, 350–359.Chedid, F. B. (2010). Sophistication and logical depth revisited. In 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) (pp. 1–4). IEEE.Cheeseman, P., Kanefsky, B. & Taylor, W. M. (1991). Where the really hard problems are. In Proceedings of IJCAI-1991 (pp. 331–337).Dastani, M. (2008). 2APL: A practical agent programming language. Autonomous Agents and Multi-agent Systems, 16(3), 214–248.Delahaye, J. P. & Zenil, H. (2011). Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness. Applied Mathematics and Computation, 219(1), 63–77Dowe, D. L. (2008). Foreword re C. S. Wallace. Computer Journal, 51(5), 523–560. Christopher Stewart WALLACE (1933–2004) memorial special issue.Dowe, D. L., & Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Du, D. Z., & Ko, K. I. (2011). Theory of computational complexity (Vol. 58). London: Wiley-Interscience.Elo, A. E. (1978). The rating of chessplayers, past and present (Vol. 3). London: Batsford.Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. London: Lawrence Erlbaum.Fatès, N. & Chevrier, V. (2010). How important are updating schemes in multi-agent systems? an illustration on a multi-turmite model. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1 (pp. 533–540). International Foundation for Autonomous Agents and Multiagent Systems.Ferber, J. & Müller, J. P. (1996). Influences and reaction: A model of situated multiagent systems. In Proceedings of Second International Conference on Multi-Agent Systems (ICMAS-96) (pp. 72–79).Ferrando, P. J. (2009). Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Applied Psychological Measurement, 33(1), 9–24.Ferrando, P. J. (2012). Assessing the discriminating power of item and test scores in the linear factor-analysis model. Psicológica, 33, 111–139.Gent, I. P., & Walsh, T. (1994). Easy problems are sometimes hard. Artificial Intelligence, 70(1), 335–345.Gershenson, C. & Fernandez, N. (2012). Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales. Complexity, 18(2), 29–44.Gruner, S. (2010). Mobile agent systems and cellular automata. Autonomous Agents and Multi-agent Systems, 20(2), 198–233.Hardman, D. K., & Payne, S. J. (1995). Problem difficulty and response format in syllogistic reasoning. The Quarterly Journal of Experimental Psychology, 48(4), 945–975.He, J., Reeves, C., Witt, C., & Yao, X. (2007). A note on problem difficulty measures in black-box optimization: Classification, realizations and predictability. Evolutionary Computation, 15(4), 435–443.Hernández-Orallo, J. (2000). Beyond the turing test. Journal of Logic Language & Information, 9(4), 447–466.Hernández-Orallo, J. (2000). On the computational measurement of intelligence factors. In A. Meystel (Ed.), Performance metrics for intelligent systems workshop (pp. 1–8). Gaithersburg, MD: National Institute of Standards and Technology.Hernández-Orallo, J. (2000). Thesis: Computational measures of information gain and reinforcement in inference processes. AI Communications, 13(1), 49–50.Hernández-Orallo, J. (2010). A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In M. Hutter et al. (Ed.), 3rd International Conference on Artificial General Intelligence (pp. 182–183). Atlantis Press Extended report at http://users.dsic.upv.es/proy/anynt/unbiased.pdf .Hernández-Orallo, J., & Dowe, D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence, 174(18), 1508–1539.Hernández-Orallo, J., Dowe, D. L., España-Cubillo, S., Hernández-Lloreda, M. V., & Insa-Cabrera, J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), LNAI series on artificial general intelligence 2011 (Vol. 6830, pp. 82–91). Berlin: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2014). Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research, 27, 50–74.Hernández-Orallo, J., Insa, J., Dowe, D. L. & Hibbard, B. (2012). Turing tests with turing machines. In A. Voronkov (Ed.), The Alan Turing Centenary Conference, Turing-100, Manchester, 2012, volume 10 of EPiC Series (pp. 140–156).Hernández-Orallo, J. & Minaya-Collado, N. (1998). A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In Proceedings of International Symposium of Engineering of Intelligent Systems (EIS’98) (pp. 146–163). ICSC Press.Hibbard, B. (2009). Bias and no free lunch in formal measures of intelligence. Journal of Artificial General Intelligence, 1(1), 54–61.Hoos, H. H. (1999). Sat-encodings, search space structure, and local search performance. In 1999 International Joint Conference on Artificial Intelligence (Vol. 16, pp. 296–303).Insa-Cabrera, J., Benacloch-Ayuso, J. L., & Hernández-Orallo, J. (2012). On measuring social intelligence: Experiments on competition and cooperation. In J. Bach, B. Goertzel, & M. Iklé (Eds.), AGI, volume 7716 of lecture notes in computer science (pp. 126–135). Berlin: Springer.Insa-Cabrera, J., Dowe, D. L., España-Cubillo, S., Hernández-Lloreda, M. V., & Hernández-Orallo, J. (2011). Comparing humans and AI agents. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), LNAI series on artificial general intelligence 2011 (Vol. 6830, pp. 122–132). Berlin: Springer.Knuth, D. E. (1973). Sorting and searching, volume 3 of the art of computer programming. Reading, MA: Addison-Wesley.Kotovsky, K., & Simon, H. A. (1990). What makes some problems really hard: Explorations in the problem space of difficulty. Cognitive Psychology, 22(2), 143–183.Legg, S. (2008). Machine super intelligence. PhD thesis, Department of Informatics, University of Lugano, June 2008.Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.Leonetti, M. & Iocchi, L. (2010). Improving the performance of complex agent plans through reinforcement learning. In Proceedings of the 2010 International Conference on Autonomous Agents and Multiagent Systems (Vol. 1, pp. 723–730). International Foundation for Autonomous Agents and Multiagent Systems.Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Levin, L. A. (1986). Average case complete problems. SIAM Journal on Computing, 15, 285.Li, M., & Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed.). Berlin: Springer.Low, C. K., Chen, T. Y., & Rónnquist, R. (1999). Automated test case generation for bdi agents. Autonomous Agents and Multi-agent Systems, 2(4), 311–332.Madden, M. G., & Howley, T. (2004). Transfer of experience between reinforcement learning environments with progressive difficulty. Artificial Intelligence Review, 21(3), 375–398.Mellenbergh, G. J. (1994). Generalized linear item response theory. Psychological Bulletin, 115(2), 300.Michel, F. (2004). Formalisme, outils et éléments méthodologiques pour la modélisation et la simulation multi-agents. PhD thesis, Université des sciences et techniques du Languedoc, Montpellier.Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.Orponen, P., Ko, K. I., Schöning, U., & Watanabe, O. (1994). Instance complexity. Journal of the ACM (JACM), 41(1), 96–121.Simon, H. A., & Kotovsky, K. (1963). Human acquisition of concepts for sequential patterns. Psychological Review, 70(6), 534.Team, R., et al. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Whiteson, S., Tanner, B., & White, A. (2010). The reinforcement learning competitions. The AI Magazine, 31(2), 81–94.Wiering, M., & van Otterlo, M. (Eds.). (2012). Reinforcement learning: State-of-the-art. Berlin: Springer.Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.Zatuchna, Z., & Bagnall, A. (2009). Learning mazes with aliasing states: An LCS algorithm with associative perception. Adaptive Behavior, 17(1), 28–57.Zenil, H. (2010). Compression-based investigation of the dynamical properties of cellular automata and other systems. Complex Systems, 19(1), 1–28.Zenil, H. (2011). Une approche expérimentale à la théorie algorithmique de la complexité. PhD thesis, Dissertation in fulfilment of the degree of Doctor in Computer Science, Université de Lille.Zenil, H., Soler-Toscano, F., Delahaye, J. P. & Gauvrit, N. (2012). Two-dimensional kolmogorov complexity and validation of the coding theorem method by compressibility. arXiv, preprint arXiv:1212.6745

    The GRONORUN 2 study: effectiveness of a preconditioning program on preventing running related injuries in novice runners. The design of a randomized controlled trial

    Get PDF
    Background: Distance running is a popular recreational exercise. It is a beneficial activity for health and well being. However, running may also cause injuries, especially of the lower extremities. In literature there is no agreement what intrinsic and extrinsic factors cause running related injuries (RRIs). In theory, most RRIs are elicited by training errors, this too much, too soon. In a preconditioning program runners can adapt more gradually to the high mechanical loads of running and will be less susceptible to RRIs. In this study the effectiveness of a 4-week preconditioning program on the incidence of RRIs in novice runners prior to a training program will be studied. Methods/Design: The GRONORUN 2 (Groningen Novice Running) study is a two arm randomized controlled trial studying the effect of a 4-week preconditioning (PRECON) program in a group of novice runners. All participants wanted to train for the recreational Groningen 4-Mile running event. The PRECON group started a 4-week preconditioning program with walking and hopping exercises 4 weeks before the start of the training program. The control (CON) and PRECON group started a frequently used 9-week training program in preparation for the Groningen 4-Mile running event. During the follow up period participants registered their running exposure, other sporting activities and running related injuries in an Internet based running log. The primary outcome measure was the number of RRIs. RRI was defined as a musculoskeletal ailment or complaint of the lower extremities or back causing a restriction on running for at least three training sessions. Discussion: The GRONORUN 2 study will add important information to the existing running science. The concept of preconditioning is easy to implement in existing training programs and will hopefully prevent RRIs especially in novice runners

    Comparative genomics of prevaccination and modern Bordetella pertussis strains

    Get PDF
    Contains fulltext : 89571.pdf (publisher's version ) (Open Access)BACKGROUND: Despite vaccination since the 1950s, pertussis has persisted and resurged. It remains a major cause of infant death worldwide and is the most prevalent vaccine-preventable disease in developed countries. The resurgence of pertussis has been associated with the expansion of Bordetella pertussis strains with a novel allele for the pertussis toxin (Ptx) promoter, ptxP3, which have replaced resident ptxP1 strains. Compared to ptxP1 strains, ptxP3 produce more Ptx resulting in increased virulence and immune suppression. To elucidate how B. pertussis has adapted to vaccination, we compared genome sequences of two ptxP3 strains with four strains isolated before and after the introduction vaccination. RESULTS: The distribution of SNPs in regions involved in transcription and translation suggested that changes in gene regulation play an important role in adaptation. No evidence was found for acquisition of novel genes. Modern strains differed significantly from prevaccination strains, both phylogenetically and with respect to particular alleles. The ptxP3 strains were found to have diverged recently from modern ptxP1 strains. Differences between ptxP3 and modern ptxP1 strains included SNPs in a number of pathogenicity-associated genes. Further, both gene inactivation and reactivation was observed in ptxP3 strains relative to modern ptxP1 strains. CONCLUSIONS: Our work suggests that B. pertussis adapted by successive accumulation of SNPs and by gene (in)activation. In particular changes in gene regulation may have played a role in adaptation

    Genome-Wide Profiling of Histone H3 Lysine 4 and Lysine 27 Trimethylation Reveals an Epigenetic Signature in Prostate Carcinogenesis

    Get PDF
    BACKGROUND: Increasing evidence implicates the critical roles of epigenetic regulation in cancer. Very recent reports indicate that global gene silencing in cancer is associated with specific epigenetic modifications. However, the relationship between epigenetic switches and more dynamic patterns of gene activation and repression has remained largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Genome-wide profiling of the trimethylation of histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) was performed using chromatin immunoprecipitation coupled with whole genome promoter microarray (ChIP-chip) techniques. Comparison of the ChIP-chip data and microarray gene expression data revealed that loss and/or gain of H3K4me3 and/or H3K27me3 were strongly associated with differential gene expression, including microRNA expression, between prostate cancer and primary cells. The most common switches were gain or loss of H3K27me3 coupled with low effect on gene expression. The least prevalent switches were between H3K4me3 and H3K27me3 coupled with much higher fractions of activated and silenced genes. Promoter patterns of H3K4me3 and H3K27me3 corresponded strongly with coordinated expression changes of regulatory gene modules, such as HOX and microRNA genes, and structural gene modules, such as desmosome and gap junction genes. A number of epigenetically switched oncogenes and tumor suppressor genes were found overexpressed and underexpressed accordingly in prostate cancer cells. CONCLUSIONS/SIGNIFICANCE: This work offers a dynamic picture of epigenetic switches in carcinogenesis and contributes to an overall understanding of coordinated regulation of gene expression in cancer. Our data indicate an H3K4me3/H3K27me3 epigenetic signature of prostate carcinogenesis

    Uptake of the Necrotic Serpin in Drosophila melanogaster via the Lipophorin Receptor-1

    Get PDF
    The humoral response to fungal and Gram-positive infections is regulated by the serpin-family inhibitor, Necrotic. Following immune-challenge, a proteolytic cascade is activated which signals through the Toll receptor. Toll activation results in a range of antibiotic peptides being synthesised in the fat-body and exported to the haemolymph. As with mammalian serpins, Necrotic turnover in Drosophila is rapid. This serpin is synthesised in the fat-body, but its site of degradation has been unclear. By “freezing” endocytosis with a temperature sensitive Dynamin mutation, we demonstrate that Necrotic is removed from the haemolymph in two groups of giant cells: the garland and pericardial athrocytes. Necrotic uptake responds rapidly to infection, being visibly increased after 30 mins and peaking at 6–8 hours. Co-localisation of anti-Nec with anti-AP50, Rab5, and Rab7 antibodies establishes that the serpin is processed through multi-vesicular bodies and delivered to the lysosome, where it co-localises with the ubiquitin-binding protein, HRS. Nec does not co-localise with Rab11, indicating that the serpin is not re-exported from athrocytes. Instead, mutations which block late endosome/lysosome fusion (dor, hk, and car) cause accumulation of Necrotic-positive endosomes, even in the absence of infection. Knockdown of the 6 Drosophila orthologues of the mammalian LDL receptor family with dsRNA identifies LpR1 as an enhancer of the immune response. Uptake of Necrotic from the haemolymph is blocked by a chromosomal deletion of LpR1. In conclusion, we identify the cells and the receptor molecule responsible for the uptake and degradation of the Necrotic serpin in Drosophila melanogaster. The scavenging of serpin/proteinase complexes may be a critical step in the regulation of proteolytic cascades
    corecore