AN EFFICIENT IDENTIFICATION OF RED BLOOD CELL EQUILIBRIUM SHAPE USING NEURAL NETWORKS

This work of applied mathematics with interfaces in bio-physics focuses on the shape identification and numerical modelisation of a single red blood cell shape. The purpose of this work is to provide a quantitative method for interpreting experimental observations of the red blood cell shape under microscopy. In this paper we give a new formulation based on classical theory of geometric shape minimization which assumes that the curvature energy with additional constraints controls the shape of the red blood cell. To minimize this energy under volume and area constraints, we propose a new hybrid algorithm which combines Particle Swarm Optimization (PSO), Gravitational Search (GSA) and Neural Network Algorithm (NNA). The results obtained using this new algorithm agree well with the experimental results given by Evans et al. (8) especially for sphered and biconcave shapes.

On Gaussian Curvature and Membrane Fission

We propose a three-dimensional mathematical model to describe dynamical processes of membrane fission. The model is based on a phase field equation that includes the Gaussian curvature contribution to the bending energy. With the addition of the Gaussian curvature energy term numerical simulations agree with the predictions that tubular shapes can break down into multiple vesicles. A dispersion relation obtained with linear analysis predicts the wavelength of the instability and the number of formed vesicles. Finally, a membrane shape diagram is obtained for the different Gaussian and bending modulus, showing different shape regimes.

The spatial extent of a single lipid’s influence on bilayer mechanics

To what spatial extent does a single lipid affect the mechanical properties of the membrane that surrounds it? The lipid composition of a membrane determines its mechanical properties. The shapes available to the membrane depend on its compositional material properties, and therefore, the lipid environment. Because each individual lipid species’ chemistry is different, it is important to know its range of influence on membrane mechanical properties. This is defined herein as the lipid’s mechanical extent. Here, a lipid’s mechanical extent is determined by quantifying lipid redistribution and the average curvature that lipid species experience on fluctuating membrane surfaces. A surprising finding is that, unlike unsaturated lipids, saturated lipids have a complicated, non-local effect on the surrounding surface, with the interaction strength maximal at a finite length-scale. The methodology provides the means to substantially enrich curvature-energy models of membrane structures, quantifying what was previously only conjecture.

Robust 1-bit compressed sensing via hinge loss minimization

This work theoretically studies the problem of estimating a structured high-dimensional signal $\boldsymbol{x}_0 \in{\mathbb{R}}^n$ from noisy $1$-bit Gaussian measurements. Our recovery approach is based on a simple convex program which uses the hinge loss function as data fidelity term. While such a risk minimization strategy is very natural to learn binary output models, such as in classification, its capacity to estimate a specific signal vector is largely unexplored. A major difficulty is that the hinge loss is just piecewise linear, so that its ‘curvature energy’ is concentrated in a single point. This is substantially different from other popular loss functions considered in signal estimation, e.g. the square or logistic loss, which are at least locally strongly convex. It is therefore somewhat unexpected that we can still prove very similar types of recovery guarantees for the hinge loss estimator, even in the presence of strong noise. More specifically, our non-asymptotic error bounds show that stable and robust reconstruction of $\boldsymbol{x}_0$ can be achieved with the optimal oversampling rate $O(m^{-1/2})$ in terms of the number of measurements $m$. Moreover, we permit a wide class of structural assumptions on the ground truth signal, in the sense that $\boldsymbol{x}_0$ can belong to an arbitrary bounded convex set $K \subset{\mathbb{R}}^n$. The proofs of our main results rely on some recent advances in statistical learning theory due to Mendelson. In particular, we invoke an adapted version of Mendelson’s small ball method that allows us to establish a quadratic lower bound on the error of the first-order Taylor approximation of the empirical hinge loss function.

Competition brings out the best: modelling the frustration between curvature energy and chain stretching energy of lyotropic liquid crystals in bicontinuous cubic phases

It is commonly considered that the frustration between the curvature energy and the chain stretching energy plays an important role in the formation of lyotropic liquid crystals in bicontinuous cubic phases. Theoretic and numeric calculations were performed for two extreme cases: parallel surfaces eliminate the variance of the chain length; constant mean curvature surfaces eliminate the variance of the mean curvature. We have implemented a model with Brakke's Surface Evolver which allows a competition between the two variances. The result shows a compromise of the two limiting geometries. With data from real systems, we are able to recover the gyroid–diamond–primitive phase sequence which was observed in experiments.