Receptor Theory in Enzymology

Rossum, J. M. van

doi:10.1016/b978-0-12-395641-5.50010-0

Cited by 9 publications

(2 citation statements)

References 70 publications

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“…Our past and ongoing forecasts, as well as [ 73 ] and [ 46 ] code for components of the systems used to generate them, are publicly available online [ 74 – 76 ]. Changes made to Delphi-Stat throughout the 2015/2016 season are described in S5 Appendix .…”

Section: Resultsmentioning

confidence: 99%

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

et al. 2018

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Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on “delta densities”, and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC’s 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

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Section: Resultsmentioning

confidence: 99%

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

et al. 2018

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“…The search for the minimum of l ( θ | N ) is performed by using the fmin_cg Python [ 25 ] function from the scipy.optimize [ 26 ] module. It uses a conjugate gradient method [ 27 ] to find a minimum for a given function.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous Maximum-Likelihood Reconstruction of Absorption Coefficient, Refractive Index and Dark-Field Scattering Coefficient in X-Ray Talbot-Lau Tomography

2016

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A maximum-likelihood reconstruction technique for X-ray Talbot-Lau tomography is presented. This technique allows the iterative simultaneous reconstruction of discrete distributions of absorption coefficient, refractive index and a dark-field scattering coefficient. This technique avoids prior phase retrieval in the tomographic projection images and thus in principle allows reconstruction from tomographic data with less than three phase steps per projection. A numerical phantom is defined which is used to evaluate convergence of the technique with regard to photon statistics and with regard to the number of projection angles and phase steps used. It is shown that the use of a random phase sampling pattern allows the reconstruction even for the extreme case of only one single phase step per projection. The technique is successfully applied to measured tomographic data of a mouse. In future, this reconstruction technique might also be used to implement enhanced imaging models for X-ray Talbot-Lau tomography. These enhancements might be suited to correct for example beam hardening and dispersion artifacts and improve overall image quality of X-ray Talbot-Lau tomography.

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Molecular Pharmacology, a Basis for Drug Design

Ariëns

1966

Fortschritte Der Arzneimittelforschung / Progress in Drug Research / Progrès Des Recherches Pharmaceutiques

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Receptor Theory in Enzymology

Cited by 9 publications

References 70 publications

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

Simultaneous Maximum-Likelihood Reconstruction of Absorption Coefficient, Refractive Index and Dark-Field Scattering Coefficient in X-Ray Talbot-Lau Tomography

Molecular Pharmacology, a Basis for Drug Design

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