scholarly journals Rapid and robust parameter inference for binary mergers

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Neil J. Cornish
Keyword(s):  
Parameter Inference ◽  
Binary Mergers ◽  
Robust Parameter

Robust Parameter Adjustment

1988 ◽  
Author(s):  
James M. Krause ◽  
Pramod P. Khargonekar
Keyword(s):  
Parameter Adjustment ◽  
Robust Parameter

scholarly journals Circulating bioactive sclerostin levels in an Austrian population-based cohort

2021 ◽  
Author(s):  
Katharina Kerschan-Schindl ◽  
Ursula Föger-Samwald ◽  
Andreas Gleiss ◽  
Stefan Kudlacek ◽  
Jacqueline Wallwitz ◽  
...  
Keyword(s):  
Serum Levels ◽  
Population Based ◽  
Negative Regulator ◽  
Total Serum ◽  
Mineral Density ◽  
Cross Sectional ◽  
Robust Parameter ◽  
Austrian Population ◽  
Different Levels

Summary Background Circulating serum sclerostin levels are supposed to give a good estimation of the levels of this negative regulator of bone mass within bone. Most studies evaluating total serum sclerostin found different levels in males compared to females and in older compared to younger subjects. Besides an ELISA detecting total sclerostin an ELISA determining bioactive sclerostin has been developed. The aim of this study was to investigate serum levels of bioactive sclerostin in an Austrian population-based cohort. Methods We conducted a cross-sectional observational study in 235 healthy subjects. Using the bioactive ELISA assay (Biomedica) bioactive sclerostin levels were evaluated. Results Serum levels of bioactive sclerostin were higher in men than in women (24%). The levels correlated positively with age (r = 0.47). A positive correlation could also be detected with body mass index and bone mineral density. Conclusion Using the ELISA detecting bioactive sclerostin our results are consistent with data in the literature obtained by different sclerostin assays. The determination of sclerostin concentrations in peripheral blood thus appears to be a robust parameter of bone metabolism.

scholarly journals Neural network aided approximation and parameter inference of non-Markovian models of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingchao Jiang ◽  
Xiaoming Fu ◽  
Shifu Yan ◽  
Runlai Li ◽  
Wenli Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Dynamics ◽  
Stochastic Simulations ◽  
Parameter Inference ◽  
Biochemical Processes ◽  
Markovian Models ◽  
The Neural Network ◽  
Large Numbers ◽  
Small Set ◽  
Noisy Measurements

AbstractNon-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

scholarly journals Driven black holes: from Kolmogorov scaling to turbulent wakes

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Tomas Andrade ◽  
Christiana Pantelidou ◽  
Julian Sonner ◽  
Benjamin Withers
Keyword(s):  
Nonlinear Dynamics ◽  
Black Holes ◽  
General Relativity ◽  
Black Hole ◽  
Strong Field ◽  
De Sitter ◽  
Event Horizons ◽  
Binary Mergers ◽  
Tidal Deformations ◽  
Proof Of Principle

Abstract General relativity governs the nonlinear dynamics of spacetime, including black holes and their event horizons. We demonstrate that forced black hole horizons exhibit statistically steady turbulent spacetime dynamics consistent with Kolmogorov’s theory of 1941. As a proof of principle we focus on black holes in asymptotically anti-de Sitter spacetimes in a large number of dimensions, where greater analytic control is gained. We focus on cases where the effective horizon dynamics is restricted to 2+1 dimensions. We also demonstrate that tidal deformations of the horizon induce turbulent dynamics. When set in motion relative to the horizon a deformation develops a turbulent spacetime wake, indicating that turbulent spacetime dynamics may play a role in binary mergers and other strong-field phenomena.

scholarly journals Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins

Biometrics ◽  
2021 ◽  
Author(s):  
H. F. Fisher ◽  
R. J. Boys ◽  
C. S. Gillespie ◽  
C. J. Proctor ◽  
A. Golightly
Keyword(s):  
Kinetic Model ◽  
Parameter Inference
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