1,725 research outputs found
Branched Latent Neural Maps
We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional
input-output maps encoding complex physical processes. A BLNM is defined by a
simple and compact feedforward partially-connected neural network that
structurally disentangles inputs with different intrinsic roles, such as the
time variable from model parameters of a differential equation, while
transferring them into a generic field of interest. BLNMs leverage latent
outputs to enhance the learned dynamics and break the curse of dimensionality
by showing excellent generalization properties with small training datasets and
short training times on a single processor. Indeed, their generalization error
remains comparable regardless of the adopted discretization during the testing
phase. Moreover, the partial connections significantly reduce the number of
tunable parameters. We show the capabilities of BLNMs in a challenging test
case involving electrophysiology simulations in a biventricular cardiac model
of a pediatric patient with hypoplastic left heart syndrome. The model includes
a 1D Purkinje network for fast conduction and a 3D heart-torso geometry.
Specifically, we trained BLNMs on 150 in silico generated 12-lead
electrocardiograms (ECGs) while spanning 7 model parameters, covering
cell-scale and organ-level. Although the 12-lead ECGs manifest very fast
dynamics with sharp gradients, after automatic hyperparameter tuning the
optimal BLNM, trained in less than 3 hours on a single CPU, retains just 7
hidden layers and 19 neurons per layer. The resulting mean square error is on
the order of on a test dataset comprised of 50 electrophysiology
simulations. In the online phase, the BLNM allows for 5000x faster real-time
simulations of cardiac electrophysiology on a single core standard computer and
can be used to solve inverse problems via global optimization in a few seconds
of computational time
Inorganic mercury sequestration by a poly(ethylene imine) dendrimer in aqueous solution
The interaction of the G-2 poly(ethylene imine) dendrimer L, derived from ammonia as initiating core, with Hg(II) and HgCl42â was studied in aqueous solution by means of potentiometric (pH-metric) measurements. Speciation of these complex systems showed that L is able to form a wide variety of complexes including 1:1, 2:1, 3:1 and 3:2 metal-to-ligand species, of different protonation states, as well as the anion complexes [(H7L)HgCl4]5+ and [(H8L)HgCl4]6+. The stability of the metal complexes is very high, making L an excellent sequestering agent for Hg(II), over a large pH range, and a promising ligand for the preparation of functionalized activated carbons to be employed in the remediation and the prevention of environmental problems
Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Cardiac digital twins provide a physics and physiology informed framework to
deliver predictive and personalized medicine. However, high-fidelity
multi-scale cardiac models remain a barrier to adoption due to their extensive
computational costs and the high number of model evaluations needed for
patient-specific personalization. Artificial Intelligence-based methods can
make the creation of fast and accurate whole-heart digital twins feasible. In
this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to
learn the temporal pressure-volume dynamics of a heart failure patient. Our
surrogate model based on LNODEs is trained from 400 3D-0D whole-heart
closed-loop electromechanical simulations while accounting for 43 model
parameters, describing single cell through to whole organ and cardiovascular
hemodynamics. The trained LNODEs provides a compact and efficient
representation of the 3D-0D model in a latent space by means of a feedforward
fully-connected Artificial Neural Network that retains 3 hidden layers with 13
neurons per layer and allows for 300x real-time numerical simulations of the
cardiac function on a single processor of a standard laptop. This surrogate
model is employed to perform global sensitivity analysis and robust parameter
estimation with uncertainty quantification in 3 hours of computations, still on
a single processor. We match pressure and volume time traces unseen by the
LNODEs during the training phase and we calibrate 4 to 11 model parameters
while also providing their posterior distribution. This paper introduces the
most advanced surrogate model of cardiac function available in the literature
and opens new important venues for parameter calibration in cardiac digital
twins
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor
An experiment-informed signal transduction model for the role of the Staphylococcus aureus MecR1 protein in ÎČ-lactam resistance
The treatment of hospital- and community-associated infections by methicillin-resistant Staphylococcus aureus (MRSA) is a perpetual challenge. This Gram-positive bacterium is resistant specifically to ÎČ-lactam antibiotics, and generally to many other antibacterial agents. Its resistance mechanisms to ÎČ-lactam antibiotics are activated only when the bacterium encounters a ÎČ-lactam. This activation is regulated by the transmembrane sensor/signal transducer proteins BlaR1 and MecR1. Neither the transmembrane/metalloprotease domain, nor the complete MecR1 and BlaR1 proteins, are isolatable for mechanistic study. Here we propose a model for full-length MecR1 based on homology modeling, residue coevolution data, a new extensive experimental mapping of transmembrane topology, partial structures, molecular simulations, and available NMR data. Our model defines the metalloprotease domain as a hydrophilic transmembrane chamber effectively sealed by the apo-sensor domain. It proposes that the amphipathic helices inserted into the gluzincin domain constitute the route for transmission of the ÎČ-lactam-binding event in the extracellular sensor domain, to the intracellular and membrane-embedded zinc-containing active site. From here, we discuss possible routes for subsequent activation of proteolytic action. This study provides the first coherent model of the structure of MecR1, opening routes for future functional investigations on how ÎČ-lactam binding culminates in the proteolytic degradation of MecI.Fil: Belluzo, Bruno Salvador. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Instituto de BiologĂa Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias BioquĂmicas y FarmacĂ©uticas. Instituto de BiologĂa Molecular y Celular de Rosario; ArgentinaFil: Abriata, Luciano Andres. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Instituto de BiologĂa Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias BioquĂmicas y FarmacĂ©uticas. Instituto de BiologĂa Molecular y Celular de Rosario; Argentina. Ăcole Polytechnique FĂ©dĂ©rale de Lausanne; SuizaFil: Giannini, EstefanĂa. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Instituto de BiologĂa Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias BioquĂmicas y FarmacĂ©uticas. Instituto de BiologĂa Molecular y Celular de Rosario; ArgentinaFil: Mihovilcevic, Damila. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Instituto de BiologĂa Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias BioquĂmicas y FarmacĂ©uticas. Instituto de BiologĂa Molecular y Celular de Rosario; ArgentinaFil: Dal Peraro, Matteo. Ăcole Polytechnique FĂ©dĂ©rale de Lausanne; SuizaFil: Llarrull, Leticia Irene. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Instituto de BiologĂa Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias BioquĂmicas y FarmacĂ©uticas. Instituto de BiologĂa Molecular y Celular de Rosario; Argentin
A comprehensive and biophysically detailed computational model of the whole human heart electromechanics
While ventricular electromechanics is extensively studied, four-chamber heart
models have only been addressed recently; most of these works however neglect
atrial contraction. Indeed, as atria are characterized by a complex physiology
influenced by the ventricular function, developing computational models able to
capture the physiological atrial function and atrioventricular interaction is
very challenging. In this paper, we propose a biophysically detailed
electromechanical model of the whole human heart that considers both atrial and
ventricular contraction. Our model includes: i) an anatomically accurate
whole-heart geometry; ii) a comprehensive myocardial fiber architecture; iii) a
biophysically detailed microscale model for the active force generation; iv) a
0D closed-loop model of the circulatory system; v) the fundamental interactions
among the different core models; vi) specific constitutive laws and model
parameters for each cardiac region. Concerning the numerical discretization, we
propose an efficient segregated-intergrid-staggered scheme and we employ
recently developed stabilization techniques that are crucial to obtain a stable
formulation in a four-chamber scenario. We are able to reproduce the healthy
cardiac function for all the heart chambers, in terms of pressure-volume loops,
time evolution of pressures, volumes and fluxes, and three-dimensional cardiac
deformation, with unprecedented matching (to the best of our knowledge) with
the expected physiology. We also show the importance of considering atrial
contraction, fibers-stretch-rate feedback and suitable stabilization
techniques, by comparing the results obtained with and without these features
in the model. The proposed model represents the state-of-the-art
electromechanical model of the iHEART ERC project and is a fundamental step
toward the building of physics-based digital twins of the human heart
A Numerical Study of Atrial Fibrillation Electrophysiological Substrate based on High-Density Mapping. Abstract
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