Biomechanics of single cortical neurons

This study presents experimental results and computational analysis of the large strain dynamic behavior of single neurons in vitro with the objective of formulating a novel quantitative framework for the biomechanics of cortical neurons. Relying on the atomic force microscopy (AFM) technique, novel testing protocols are developed to enable the characterization of neural soma deformability over a range of indentation rates spanning three orders of magnitude, 10, 1, and 0.1 μm s[superscript −1]. Modified spherical AFM probes were utilized to compress the cell bodies of neonatal rat cortical neurons in load, unload, reload and relaxation conditions. The cell response showed marked hysteretic features, strong non-linearities, and substantial time/rate dependencies. The rheological data were complemented with geometrical measurements of cell body morphology, i.e. cross-diameter and height estimates. A constitutive model, validated by the present experiments, is proposed to quantify the mechanical behavior of cortical neurons. The model aimed to correlate empirical findings with measurable degrees of (hyper)elastic resilience and viscosity at the cell level. The proposed formulation, predicated upon previous constitutive model developments undertaken at the cortical tissue level, was implemented in a three-dimensional finite element framework. The simulated cell response was calibrated to the experimental measurements under the selected test conditions, providing a novel single cell model that could form the basis for further refinements.Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (DAAD-19-02-D-002)Joint Improvised Explosive Device Defeat Organization (U.S.) (W911NF-07-1-0035)National Science Foundation (U.S.). Graduate Research FellowshipNational Institutes of Health (U.S.) (Molecular, Cell, and Tissue Biomechanics Training Grant)Ecole des ponts et chaussees (France)Computation and Systems Biology Programme of Singapore--Massachusetts Institute of Technology Allianc

Adaptive sampling strategies for reduced-order modeling

Reduced-order models (ROMs) become increasingly popular in industrial design and optimization processes, since they allow to approximate expensive high fidelity computational fluid dynamics (CFD) simulations in near real-time. The quality of ROM predictions highly depends on the placement samples in the spanned parameter space. Adaptive sampling strategies allow to identify regions of interest, which feature e.g. nonlinear responses with respect to the parameters, and therefore enable the sensible placement of new samples. By introducing more samples in these regions, the ROM prediction accuracy should increase. In this contribution we investigate different adaptive sampling strategies based on cross-validation, Gaussian mean-squared error, two methods exploiting the CFD residual and a two manifold embedding methods. The performance of those strategies is evaluated and measured by their ability to successfully identify the regions of interest and the resulting sample placement in terms of different quantitative statistical values. We further discuss the reduction of the ROM prediction error over the adaptive sampling iterations and show that depending on the adaptive sampling strategy, the number of required samples can be reduced by 35-44% without deteriorating model quality compared to a Halton sequence sampling plan

Interpolation-based reduced-order modelling for steady transonic flows via manifold learning

This paper presents a parametric reduced-order model (ROM) based on manifold learning (ML) for use in steady transonic aerodynamic applications. The main objective of this work is to derive an efficient ROM that exploits the low-dimensional nonlinear solution manifold to ensure an improved treatment of the nonlinearities involved in varying the inflow conditions to obtain an accurate prediction of shocks. The reduced-order representation of the data is derived using the Isomap ML method, which is applied to a set of sampled computational fluid dynamics (CFD) data. In order to develop a ROM that has the ability to predict approximate CFD solutions at untried parameter combinations, Isomap is coupled with an interpolation method to capture the variations in parameters like the angle of attack or the Mach number. Furthermore, an approximate local inverse mapping from the reduced-order representation to the full CFD solution space is introduced. The proposed ROM, called Isomap+I, is applied to the two-dimensional NACA 64A010 airfoil and to the 3D LANN wing. The results are compared to those obtained by proper orthogonal decomposition plus interpolation (POD+I) and to the full-order CFD model

CFD-based ROMs for aeronautical applications

The advent and development of large-scale high-fidelity computational fluid dynamics (CFD) in aircraft design is requiring, more and more, procedures and techniques aimed at reducing its computational cost in order to afford accurate but fast simulations of, e.g., the aerodynamic loads. The adoption of reduced order modeling techniques in CFD represents a promising approach to achieve this goal. Several methods have been developed to obtain reduced order models (ROMs) for the prediction of steady and unsteady aerodynamic flows using low-dimensional linear subspaces as well as nonlinear manifolds, whose performances may be further improved by applying hyper-reduction procedures. In this talk, it is presented the activity done at the German Aerospace Center (DLR) in the context of model order reduction and surrogate modeling for multidisciplinary applications, design and optimization. Different examples are shown to demonstrate the use of the ROMs in aeronautical applications, as for fusing experimental and CFD data, accelerating CFD computations, obtaining a fast loads prediction across the flight envelope, or accelerating multidisciplinary optimizations. The ROMs approches are demonstrated for airfoils, wings and aircraft in subsonic and transonic flows

Reduced Order Models for Aerodynamic Applications, Loads and MDO

This work gives an overview of Reduced Order Model (ROM) applications employed within the context of the DLR Digital-X project. The ROM methodology has found widespread application in fluid dynamics. In its direct application to CFD it seeks to reduce the computational complexity of a problem by reducing the number of degrees of freedom rather than simplifying the physical model. Here, parametric aerodynamic ROMs are used to provide pressure distributions based on high-fidelity CFD, but at lower evaluation time and storage than the original CFD model. ROMs for steady aerodynamic applications are presented. We consider ROMs combining proper orthogonal decomposition (POD) and Isomap, which is a manifold learning method, with interpolation methods as well as physics-based ROMs, where an approximate solution is found in the POD-subspace or non-linear manifold by minimizing the corresponding steady or unsteady flow-solver residual. The issue of how to train the ROM with high-fidelity CFD data is also addressed. The steady ROMs are used to predict the static aeroelastic loads in an MDO context, where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. We also show an approach combining correction of a linear loads analysis model using steady, rigid CFD solutions at various Mach numbers and angles of attack with a ROM of the corrected Aerodynamic Influence Coefficients (AICs). This integrates the results into a complete loads analysis model preserving aerodynamic nonlinearities while allowing fast evaluation across all model parameters. Thus, correction for the major nonlinearities, e.g. depending on Mach number and angle of attack combines with the linearity of the baseline model to yield a large domain of validity across all flow parameters at the expense of a relatively small number of CFD solutions. The different ROM methods are applied to 3D test cases and in particular to a transonic wing-body transport aircraft configuration

Reduced-order models for aerodynamic applications, loads and MDO

This article gives an overview of reduced-order modeling work performed in the DLR project Digital-X. Parametric aerodynamic reduced-order models (ROMs) are used to predict surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady aerodynamic applications are built using proper orthogonal decomposition and Isomap, a manifold learning method. Approximate solutions in the so-obtained low-dimensional representations of the data are found with interpolation techniques, or by minimizing the corresponding steady flow-solver residual. The latter approach produces physics-based ROMs driven by the governing equations. The steady ROMs are used to predict the static aeroelastic loads in a multidisciplinary design and optimization context, where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. An approach to correct a linear loads analysis model using steady CFD solutions at various Mach numbers and angles of attack and a ROM of the corrected aerodynamic influence coefficients is also shown. This results in a complete loads analysis model preserving aerodynamic nonlinearities while allowing fast evaluation across all model parameters. The different ROM methods are applied to a 3D test case of a transonic wing-body transport aircraft configuration

The "efficacy-effectiveness gap" : Historical background and current conceptualization

Background The concept of the "efficacy-effectiveness gap" (EEG) has started to challenge confidence in decisions made for drugs when based on randomized controlled trials alone. Launched by the Innovative Medicines Initiative, the GetReal project aims to improve understanding of how to reconcile evidence to support efficacy and effectiveness and at proposing operational solutions. Objectives The objectives of the present narrative review were 1) to understand the historical background in which the concept of the EEG has emerged and 2) to describe the conceptualization of EEG. Methods A focused literature review was conducted across the gray literature and articles published in English reporting insights on the EEG concept. The identification of different "paradigms" was performed by simple inductive analysis of the documents' content. Results The literature on the EEG falls into three major paradigms, in which EEG is related to 1) real-life characteristics of the health care system; 2) the method used to measure the drug's effect; and 3) a complex interaction between the drug's biological effect and contextual factors. Conclusions The third paradigm provides an opportunity to look beyond any dichotomy between "standardized" versus "real-life" characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs' effectiveness

The "efficacy-effectiveness gap" : Historical background and current conceptualization

Background The concept of the "efficacy-effectiveness gap" (EEG) has started to challenge confidence in decisions made for drugs when based on randomized controlled trials alone. Launched by the Innovative Medicines Initiative, the GetReal project aims to improve understanding of how to reconcile evidence to support efficacy and effectiveness and at proposing operational solutions. Objectives The objectives of the present narrative review were 1) to understand the historical background in which the concept of the EEG has emerged and 2) to describe the conceptualization of EEG. Methods A focused literature review was conducted across the gray literature and articles published in English reporting insights on the EEG concept. The identification of different "paradigms" was performed by simple inductive analysis of the documents' content. Results The literature on the EEG falls into three major paradigms, in which EEG is related to 1) real-life characteristics of the health care system; 2) the method used to measure the drug's effect; and 3) a complex interaction between the drug's biological effect and contextual factors. Conclusions The third paradigm provides an opportunity to look beyond any dichotomy between "standardized" versus "real-life" characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs' effectiveness