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Presentation by Liam Ruske at CECAM Mixed-Gen and Fundamentals of Growing Active Matter Workshop

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.
Liam Ruske has taken the opportunity to present and discuss his work on three-dimensional organisation and morphology of active droplets at the CECAM Mixed-Gen series on March 4 and the Fundamental of Growing Active Matter workshop on March 25.

A lot is understood about the ways in which single cells move, but there are still many questions about the motion and organisation of cell aggregates where cells coupled through intercellular junctions show a range of collective behaviours.

This work, which has been recently published Phys. Rev. X 11, 021001 (2021), shows the potential of active nematic continuum models to describe collective cell motion in a three dimensional environment.

Popular Summary:

Active matter describes systems—living and synthetic—where a continuous influx of energy at the level of individual components leads to striking collective behavior among the individual components, such as self-organizing bacteria colonies, bird flocks, or polymers in the cytoskeleton of cells. Understanding their behavior has attracted interest for studies of biological systems—from the spread of cancer to the development of organisms—as well the development of mesoscopic engines. Here, we numerically investigate 3D droplets composed of active matter and the ways in which their shapes change in response to the continuous input of energy.

One striking observation is the continuous formation of fingerlike protrusions, reminiscent of the collective motion of invading cancer cells. By changing the mechanical properties of the drop or the activity level, we find several different dynamical responses: For example, the droplet surface can wrinkle in a way that resembles a walnut or the active forces can drive a dimple in the droplet to grow, leading to a cup shape. Such invagination is reminiscent of patterns seen during morphogenesis.

Understanding the behavior of model systems, here a continuum model of active material, is an important step toward the goal of understanding the role of physical theories in the life sciences.

Round Table Discussion on: Optics, Spectroscopy, Micro and Nanofabrication, and Nanotribology

On Tuesday 23 March the fourth round table of the initial training on experimental methods for active matter took place. The topic of the round table was “Optics, Spectroscopy, Micro and Nanofabrication, and Nanotribology”, and the discussion was led by Ayten Gülce Bayram , David Bronte Ciriza, Dana Hassan, Carolina van Baalen and Jesús Manuel Antúnez Domínguez.
The panelists included Maria Grazia Donato, Pietro Gucciardi, Antonino Foti, Shivaprakash Ramakrishna, and Felix Holzner.

The importance of the topic of the round table to the field of active matter was motivated by the panelists from different perspectives. The discussion ranged from the main differences and challenges that come along with working on the micro- and nanoscale, to how changing the dimensions of your system allows one to change the properties of a system’s response, as well as the challenges involved in bringing a product idea to the market. The main conclusion was that the nanoscale is exciting, but the smaller you get, the greater the challenge.

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.

Round Table Discussion on: Machine Learning

The third round table session of the experimental training was about machine learning and its role in science, in particular physics and active matter. The panelists invited to the discussion were Carlo Manzo from Vic University, Benjamin Midtvedt and Saga Helgadottir from Gothenburg University, Onofrio Maragò and Alessandro Magazzù from CNR ICPF-Messina. The discussion was organized and lead by Jesus M. A. Dominguez, Davide Breoni, Liam Ruske, Chun-Jen Chen and Alireza Khoshzaban, who are students attending the training.

The discussion touched topics like the applications of machine learning in fields like optics, biophysics, medical research, the potentialities and the reliability of the method. Questions on when a machine learning approach is advisable and how cautious one must be when applying machine learning were also addressed. Current important logical and practical aspects of the method were also discussed, together with the need of testing machine learning applications against more classical ones. The panelists also stated the importance of reliably checking the results obtained to avoid biases that can lead to false conclusions.

After one hour of fruitful discussion we gained a broader perspective and a deeper understanding of machine learning.

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.

Round Table Discussion on: Living Active Matter

As part of the experimental training, a second round-table discussion took place yesterday, 18 March 2021. The event centered around a discussion on the topic of « Living Active Matter » and featured four invited guests, all physicists, who have studied different topics and length scales relevant to living systems. The invited panel was composed of Aidan Brown from University of Edinburgh, Salima Rafai who works at CNRS in Grenoble, Eric Clément from PMMH-ESPCI in Paris and Benjamin Friedrich from TU Dresden, and was conducted by six of the students attending the training: Audrey Nsamela, David Bronte, Jérémie Bertrand, Ojus Satish Bagal, Daniela Peréz Guerrero and Dana Hassan, who first introduced each guest and then asked selected questions. From molecules and cells to tissues, organisms and populations, each guest had a particular expertise which made for a wide-ranging and interesting discussion.

A question on the evolutionary role of self-propulsion was met with an answer from Dr Brown, who, as obvious as it may seem, pointed out that organisms become “active” when whatever they need to survive is not in their immediate surroundings and must be found elsewhere. When Dr Rafai suggested that the micro-swimmers she studied were not converting their energy to motion optimally, Dr Brown pointed out that biological systems are optimized only in the sense that they are versatile and can adapt to a large number of situations or physical parameters, which is not something that can be captured by a single experiment. This goes to show that, when given the same set of facts, physicists and biologists will often interpret their observations differently, and that discussions between the two disciplines can be fruitful.

Dr Friedrich pointed out that the inherent complexity of biology was such that you could sometimes make progress by just looking and writing down how the processes unfold. He went on to explain that one of the bigger challenges biologists face is that many of these processes occurr below the resolution limit of the microscopes (the “diffraction barrier”) and can therefore not be observed by regular optical microscopes. Several panelists are excited about the coming of newly-designed, ground-breaking microscopes; devices that would use entangled photons to break the diffraction barrier. These new technologies could help not only the field of biology, but also encourage physicists and chemists to collaborate and create models of previously undiscovered mechanisms at the smaller scales. Deep learning is another tool that was alluded to by Dr Rafai as something to look forward to for image reconstruction.

Overall, we found the discussion very productive and we would like to thank once more the panelists for their insights and willingness to participate!

Round Table Discussion on: Phoretic Propulsion Mechanism

During the second day of the experimental training, we organised the first round table discussion. The session was chaired by six of the students attending the training: Carolina van Baalen, Danne van Roon, Gülce Bayram, Harshith Bachimanchi, Laura Natali and Sandrine Heijnen.

The topic of the round table was phoretic propulsion mechanisms and we had four panelists – Juliane Simmchen, Frank Cichos, Ivo Buttinoni and Felix Ginot – and a guest speaker, Antoni Homs Corbera. After a brief introduction of the panelists, we had a chance to ask all the questions we collected from the other participants.

The discussion started with the definition of the term “phoresis” and continued with the simulation frameworks for phoretic colloids. It included a brief discussion of the complexity involved in these processes and the typical length scales at which interfacial effects are relevant.

The conclusion was “a common joke at conferences is that the phoresis starts when coffee is about to be served”. The real conclusion was that phoretic interaction needs very large gradients on the macroscopic scale and is hidden by diffusion on a very small scale.

All participants had the possibility to jump in and add upcoming questions. We ended the round table by discussing the possible applications of phoretic colloids, highlighting the environmental aspects like microplastics’ filtration in water.

We thank all the guests and participants for making it a successful discussion moment.

Talk by Chun-Jen Chen at Institute of Physics, Academia Sinica (Taiwan), 25 February 2021

The topic of this talk covered the laser experiments enabling active particle steering (upper left), collective motion of such particles (middle) and connection to social animal behaviours, eg. fish school (lower). (Image by C-J Chen.)
On the 25th of February 2021, Chun-Jen gave a talk at the Institute of Physics, Academia Sinica (Taipei, Taiwan) about his research project at University of Konstanz. He explained how active Janus micro-spheres can be propelled and steered at the indivitual level in his experiment system and how such experiments are linked to studies of collective behaviours in living systems. The talk induced vivid discussions with audience of different backgrounds. Chun-Jen also shared experiences regarding PhD life in Germany with prospective young researchers in Taiwan.

Active noise-driven particles under space-dependent friction in one dimension on arXiv

Sketch of the confining potential U(x) = κ|x|, a linear friction gradient γ(x) = γ0+γ1|x| in arbitrary units. The particle, shown by a blue dot on the x-axis, is activated by noise (indicated in red), under the influence of the potential and the friction gradient. Image by D. Breoni.
Active noise-driven particles under space-dependent friction in one dimension

Davide Breoni, Ralf Blossey, Hartmut Löwen
arxiv: 2102.09944

Abstract: We study a Langevin equation describing the stochastic motion of a particle in one dimension with coordinate x, which is simultaneously exposed to a space-dependent friction coefficient γ (x), a confining potential U(x) and non-equilibrium (i.e., active) noise. Specically, we consider frictions γ (x) = γ0 + γ1|x|p and potentials U(x) ∝ |x|p with exponents p = 1; 2 and n = 0; 1; 2. We provide analytical and numerical results for the particle dynamics for short times and the stationary
probability density functions (PDFs) for long times. The short-time behaviour displays diffusive and ballistic regimes while the stationary PDFs display unique characteristic features depending on the exponent values (p; n). The PDFs interpolate between Laplacian, Gaussian and bimodal distributions, whereby a change between these different behaviours can be achieved by a tuning of the friction strengths ratio
γ0 / γ1. Our model is relevant for molecular motors moving on a
one-dimensional track and can also be realized for confined self-propelled colloidal particles.

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.