Relative sea-level change in northeastern Florida (USA) during the last ~8.0 ka

An existing database of relative sea-level (RSL) reconstructions from the U.S. Atlantic coast lacked valid sea-level index points from Georgia and Florida. This region lies on the edge of the collapsing forebulge of the former Laurentide Ice Sheet making it an important location for understanding glacio-isostatic adjustment and the history of ice-sheet melt. To address the paucity of data, we reconstruct RSL in northeastern Florida (St. Marys) over the last ∼8.0 ka from samples of basal salt-marsh sediment that minimize the influence of compaction. The analogy between modern salt-marsh foraminifera and their fossil counterparts preserved in the sedimentary record was used to estimate paleomarsh surface elevation. Sample ages were determined by radiocarbon dating of identifiable and in-situ plant macrofossils. This approach yielded 25 new sea-level index points that constrain a ∼5.7 m rise in RSL during the last ∼8.0 ka. The record shows that no highstand in sea level occurred in this region over the period of the reconstruction. We compared the new reconstruction to Earth-ice models ICE 6G-C VM5a and ICE 6G-C VM6. There is good fit in the later part of the Holocene with VM5a and for a brief time in the earlier Holocene with VM6. However, there are discrepancies in model-reconstruction fit in the early to mid Holocene in northeastern Florida and elsewhere along the Atlantic coast at locations with early Holocene RSL reconstructions. The most pronounced feature of the new reconstruction is a slow down in the rate of RSL rise from approximately 5.0 to 3.0 ka. This trend may reflect a significant contribution from local-scale processes such as tidal-range change and/or change in base flow of the St. Marys River in response to paleoclimate changes. However, the spatial expression (local vs. regional) of this slow down is undetermined and corroborative records are needed to establish its geographical extent

Dirac mixture distributions for the approximation of mixed effects models.

Mixed effect modeling is widely used to study cell-to-cell and patient-to-patient variability. The population statistics of mixed effect models is usually approximated using Dirac mixture distributions obtained using Monte-Carlo, quasi Monte-Carlo, and sigma point methods. Here, we propose the use of a method based on the Cramér-von Mises Distance, which has been introduced in the context of filtering. We assess the accuracy of the different methods using several problems and provide the first scalability study for the Cramér-von Mises Distance method. Our results indicate that for a given number of points, the method based on the modified Cramér-von Mises Distance method tends to achieve a better approximation accuracy than Monte-Carlo and quasi Monte-Carlo methods. In contrast to sigma-point methods, the method based on the modified Cramér-von Mises Distance allows for a flexible number of points and a more accurate approximation for nonlinear problems

P287 Assessment of mitral regurgitation and mitral geometry in patients after transcatheter aortic valve implantation

Abstract Introduction Mitral regurgitation is often found in conjunction with aortic stenosis and the prevalence of both valvular lesions increases with age. Purpose The aim of this study was to evaluate mitral regurgitation, left ventricle and left atrium in patients with severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI). Methods A total of 31 patients (29% males) with severe aortic stenosis and moderate or severe mitral regurgitation, who underwent TAVI were included in this study. Clinical and echocardiographic characteristics were performed at baseline and in 6, 12 months observation. Results After TAVI, decrease of vena contracta width of mitral regurgitation (p = 0.00002, p = 0.00004), aorto-mural mitral annulus diameter (p = 0.00008, p = 0.02), increase mitral annular plane systolic excursion (p = 0.0004, p = 0.0003), left ventricular stroke volume (p = 0.0003, p = 0.0004), ejection fraction (p = 0.0004, p = 0.01) and decrease major dimension of left ventricle in three chamber view (p = 0.05, p = 0.002) was observed in patient in both time points, respectively. Additionally, we observed decrease of distance between head of papillary muscles (p = 0.003) at 6 months and decrease of left atrium indexed volume (p = 0.01) and grade of tricuspid regurgitation (p = 0.03) at 12 months follow up. Conclusions Patients with moderate or severe mitral regurgitation after TAVI procedure achieved significant reductions of mitral regurgitation and improvement of some parameters assessing mitral annulus, left ventricle and left atrium geometry. </jats:sec

Mini-batch optimization enables training of ODE models on large-scale datasets.

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible

AMICI: High-performance sensitivity analysis for large ordinary differential equation models.

SUMMARY: Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C ++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. AVAILABILITY: AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online