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.

Optical tweezers in a dusty universe published in The European Physical Journal Plus

Pictorial representation of space tweezers, space applications of optical tweezers. Interplanetary or planetary dust can be collected and investigated directly in situ (open space or extraterrestrial surfaces). The inset represents a closeup of a grain of interplanetary dust trapped by a single-beam optical tweezers. (Image by Alessandro Magazzù)
Optical tweezers in a dusty universe
P. Polimeno, A. Magazzù, M. A. Iatì, R. Saija, L. Folco, D. Bronte Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C. Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini, V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi & O. M. Maragò
Eur. Phys. J. Plus 136, 339 (2021)
doi: 10.1140/epjp/s13360-021-01316-z

Abstract:
Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate, and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications of this contactless technique have been developed in many fields. Despite this progress, optical trapping applications to planetary exploration are still to be developed. Here we describe how optical tweezers can be used to trap and characterize extraterrestrial particulate matter. In particular, we exploit light scattering theory in the T-matrix formalism to calculate radiation pressure and optical trapping properties of a variety of complex particles of astrophysical interest. Our results open perspectives in the investigation of extraterrestrial particles on our planet, in controlled laboratory experiments, aiming for space tweezers applications: optical tweezers used to trap and characterize dust particles in space or on planetary bodies surface.

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Improving epidemic testing and containment strategies using machine learning on ArXiv

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Active Brownian and inertial particles in disordered environments: short-time expansion of the mean-square displacement on ArXiv

Active Brownian and inertial particles in disordered environments: short-time expansion of the mean-square displacement
Davide Breoni, Michael Schmiedeberg, Hartmut Löwen
arXiv: 2010.11076

We consider an active Brownian particle moving in a disordered two-dimensional energy or motility landscape. The averaged mean-square-displacement (MSD) of the particle is calculated analytically within a systematic short-time expansion. As a result, for overdamped particles, both an external random force field and disorder in the self-propulsion speed induce ballistic behaviour adding to the ballistic regime of an active particle with sharp self-propulsion speed. Spatial correlations in the force and motility landscape contribute only to the cubic and higher order powers in time for the MSD. Finally, for inertial particles two superballistic regimes are found where the scaling exponent of the MSD with time is α = 3 and α = 4. We confirm our theoretical predictions by computer simulations. Moreover they are verifiable in experiments on self-propelled colloids in random environments.

Morphology of active deformable 3D droplets on ArXiv

Morphology of active deformable 3D droplets.

Morphology of active deformable 3D droplets
Liam J. Ruske, Julia M. Yeomans
arXiv: 2010.10427

We numerically investigate the morphology and disclination line dynamics of active nematic droplets in three dimensions. Although our model only incorporates the simplest possible form of achiral active stress, active nematic droplets display an unprecedented range of complex morphologies. For extensile activity finger-like 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 behaviour is explained in terms of an interplay between active anchoring, active flows and the dynamics of the motile dislocation 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.