Press release on Machine learning can help slow down future pandemics

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics

David Bronte Ciriza nominated for a Student Paper Prize at the Biophotonics Congress

Optical forces calculated on a sphere with the geometrical optics (left column) and the machine learning (center column) approaches. The difference between both approaches is shown in the column on the right, illustrating the removal of artefacts with the machine learning method.
David Bronte Ciriza has been nominated by the Optical Society of America for a Student Paper Prize for Optical Manipulation and its Applications among three other finalists. He will present his work on Machine Learning to Enhance the Calculation of Optical Forces in the Geometrical Optics Approximation at the 2021 OSA Biophotonics Congress: Optics in Life Sciences.

This work, which is the fruit of a collaboration between the groups at the CNR-IPCF and the University of Gothenburg, shows the potential of machine learning for faster and more accurate optical forces calculations. The extended abstract can be found here.

Based on the oral presentations of the finalists, the jury will select the winner. David will present on April 16th at 5:00 pm (CEST).

Presentation by D. Bronte Ciriza at OSA-OMA-2021

Optical forces calculated on a sphere with the geometrical optics (left column) and the machine learning (center column) approaches. The difference between both approaches is shown in the column on the right, illustrating the removal of artefacts with the machine learning method.
Machine learning to enhance the calculation of optical forces in the geometrical optics approximation
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: OSA-OMA-2021, AF2D.2 Contribution
Date: 16 April
Time: 17:00 CEST

Short Abstract:
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation

Extended Abstract:
Light can exert forces by exchanging momentum with particles. Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. Optical tweezers, which are instruments that, by tightly focusing a laser beam, are capable of confining particles in three dimensions, have become a common tool for manipulation of micro- and nano- particles, as well as a force and torque transducer with sensing capabilities at the femtonewton level. Optical tweezers have also been successfully employed to explore novel phenomena, including protein folding and molecular motors, or the optical forces and Brownian motion of 1D and 2D materials.

Numerical simulations play a fundamental role in the planning of experiments and in the interpretation of the results. In some basic cases for optical tweezers, the optical trap can be approximated by a harmonic potential. However, there are many situations where this approximation is insufficient, for example in the case of a particle escaping an optical trap, or for particles that are moving on an optical landscape but are not trapped. In these cases, a more complex treatment of the light-matter interaction is required for a more accurate calculation of the forces. This calculation is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way. Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis.

In this work, we explore the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is typically the case in experiments with micrometer-size particles. The optical field is described by a collection of N light rays and the momentum exchange between the rays and the particle is calculated employing the tools of geometrical optics. The limitation of considering a discrete N number of light rays introduces artifacts in the force calculation. We show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation. This is first demonstrated by training a neural network for the case of a spherical particle with 3 degrees of freedom accounting for the position of the particle. We show how the neural network improves the prediction of the force with respect to the initial training data that has been generated through the geometrical optics approach.

Starting from these results for 3 degrees of freedom, the work has been expanded to 9 degrees of freedom by including all the relevant parameters for the calculation of the optical forces considering also different refractive indexes, shapes, sizes, positions, and orientations of the particle besides different numerical apertures of the objective that focuses the light.

This work proves machine learning as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.

Morphology of active deformable 3D droplets published in Physical Review X

3D droplets composed of active matter change their shape in response to a continuous influx of energy. Active droplets display an unprecedented range of complex morphologies, from cup-shaped droplet invagination, run-and-tumble motion or surface wrinkles caused by contractile activity, to the continuous formation and retraction of finger-like protrusions driven by extensile activity.
Morphology of active deformable 3D droplets
Liam J. Ruske, Julia M. Yeomans
Phys. Rev. X 11, 021001 (2021)

Abstract:
We numerically investigate the morphology and disclination line dynamics of active nematic droplets in three dimensions. Although our model incorporates only the simplest possible form of achiral active stress, active nematic droplets display an unprecedented range of complex morphologies. For extensile activity, fingerlike protrusions grow at points where disclination lines intersect the droplet surface. For contractile activity, however, the activity field drives cup-shaped droplet invagination, run-and-tumble motion, or the formation of surface wrinkles. This diversity of behavior is explained in terms of an interplay between active anchoring, active flows, and the dynamics of the motile disclination lines. We discuss our findings in the light of biological processes such as morphogenesis, collective cancer invasion, and the shape control of biomembranes, suggesting that some biological systems may share the same underlying mechanisms as active nematic droplets.