189 research outputs found

    Performance of soybean varieties in Louisiana, 1980-1983

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    Re-interpreting conventional interval estimates taking into account bias and extra-variation

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    BACKGROUND: The study design with the smallest bias for causal inference is a perfect randomized clinical trial. Since this design is often not feasible in epidemiologic studies, an important challenge is to model bias properly and take random and systematic variation properly into account. A value for a target parameter might be said to be "incompatible" with the data (under the model used) if the parameter's confidence interval excludes it. However, this "incompatibility" may be due to bias and/or extra-variation. DISCUSSION: We propose the following way of re-interpreting conventional results. Given a specified focal value for a target parameter (typically the null value, but possibly a non-null value like that representing a twofold risk), the difference between the focal value and the nearest boundary of the confidence interval for the parameter is calculated. This represents the maximum correction of the interval boundary, for bias and extra-variation, that would still leave the focal value outside the interval, so that the focal value remained "incompatible" with the data. We describe a short example application concerning a meta analysis of air versus pure oxygen resuscitation treatment in newborn infants. Some general guidelines are provided for how to assess the probability that the appropriate correction for a particular study would be greater than this maximum (e.g. using knowledge of the general effects of bias and extra-variation from published bias-adjusted results). SUMMARY: Although this approach does not yet provide a method, because the latter probability can not be objectively assessed, this paper aims to stimulate the re-interpretation of conventional confidence intervals, and more and better studies of the effects of different biases

    Recent Developments in the General Atomic and Molecular Electronic Structure System

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    A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized

    Hierarchical Models in the Brain

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    This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain

    Allogamy-Autogamy Switch Enhance Assortative Mating in the Allotetraploid Centaurea seridis L. Coexisting with the Diploid Centaurea aspera L. and Triggers the Asymmetrical Formation of Triploid Hybrids

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    [EN] Hybridization between tetraploids and its related diploids is generally unsuccessful in Centaurea, hence natural formation of triploid hybrids is rare. In contrast, the diploid Centaurea aspera and the allotetraploid C. seridis coexist in several contact zones where a high frequency of triploid hybrids is found. We analyzed the floral biology of the three taxa to identify reproductive isolation mechanisms that allow their coexistence. Flowering phenology was recorded, and controlled pollinations within and between the three taxa were performed in the field. Ploidy level and germination of progeny were also assessed. There was a 50% flowering overlap which indicated a phenological shift. Diploids were strictly allogamous and did not display mentor effects, while tetraploids were found to be highly autogamous. This breakdown of self-incompatibility by polyploids is first described in Centaurea. The asymmetrical formation of the hybrid was also found: all the triploid intact cypselae came from the diploid mothers pollinated by the pollen of tetraploids. Pollen and eggs from triploids were totally sterile, acting as a strong triploid block. These prezygotic isolation mechanisms ensured higher assortative mating in tetraploids than in diploids, improving its persistence in the contact zones. However these mechanisms can also be the cause of the low genetic diversity and high genetic structure observed in C. seridis.Ferriol Molina, M.; Garmendia, A.; Ana Gonzalez; Merle Farinós, HB. (2015). Allogamy-Autogamy Switch Enhance Assortative Mating in the Allotetraploid Centaurea seridis L. Coexisting with the Diploid Centaurea aspera L. and Triggers the Asymmetrical Formation of Triploid Hybrids. PLoS ONE. 10(10):1-13. doi:10.1371/journal.pone.0140465S1131010Jiao, Y., Wickett, N. J., Ayyampalayam, S., Chanderbali, A. S., Landherr, L., Ralph, P. E., … dePamphilis, C. W. (2011). 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