Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion published in Nature Machine Intelligence

Input graph structure including a redundant number of edges. (Image by J. Pineda.)
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo
Nature Machine Intelligence (2023)
arXiv: 2202.06355
doi: 10.1038/s42256-022-00595-0

The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here, we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically-relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments.

Faster and more accurate geometrical-optics optical force calculation using neural networks published in ACS Photonics

Focused rays scattered by an ellipsoidal particles (left). Optical torque along y calculated in the x-y plane using ray scattering with a grid of 1600 rays (up, right) and using a trained neural network (down, right). (Image by the Authors of the manuscript.)
Faster and more accurate geometrical-optics optical force calculation using neural networks
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
ACS Photonics, 2022
doi: 10.1021/acsphotonics.2c01565
arXiv: 2209.04032

Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.

Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps published in Biomedical Optics Express

Example of final segmentation with the UNet-dm of the specular microscopy image of a severe case of cornea guttata. (Image by the Authors of the manuscript.)
Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
Juan S. Sierra, Jesus Pineda, Daniela Rueda, Alejandro Tello, Angelica M. Prada, Virgilio Galvis, Giovanni Volpe, Maria S. Millan, Lenny A. Romero, Andres G. Marrugo
Biomedical Optics Express 14, 335-351 (2023)
doi: 10.1364/BOE.477495
arXiv: 2210.07102

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs’ dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.

Active matter in space published in npj Microgravity

Effect of gravity on matter: Sedimentation and creaming. Fv and Fg represent the viscous force and gravitational force, respectively. (Image by Authors.)
Active matter in space
Giorgio Volpe, Clemens Bechinger, Frank Cichos, Ramin Golestanian, Hartmut Löwen, Matthias Sperl and Giovanni Volpe
npj Microgravity, 8, 54 (2022)
doi: 10.1038/s41526-022-00230-7

In the last 20 years, active matter has been a highly dynamic field of research, bridging fundamental aspects of non-equilibrium thermodynamics with applications to biology, robotics, and nano-medicine. Active matter systems are composed of units that can harvest and harness energy and information from their environment to generate complex collective behaviours and forms of self-organisation. On Earth, gravity-driven phenomena (such as sedimentation and convection) often dominate or conceal the emergence of these dynamics, especially for soft active matter systems where typical interactions are of the order of the thermal energy. In this review, we explore the ongoing and future efforts to study active matter in space, where low-gravity and microgravity conditions can lift some of these limitations. We envision that these studies will help unify our understanding of active matter systems and, more generally, of far-from-equilibrium physics both on Earth and in space. Furthermore, they will also provide guidance on how to use, process and manufacture active materials for space exploration and colonisation.

Recent eLife article on plankton tracking gets featured on Swedish national radio

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
The article Microplankton life histories revealed by holographic microscopy and deep learning gets featured on Vetenskapradion Nyheter (Science radio) operated by Sveriges Radio (Swedish national radio) on November 7, 2022.

The short audio feature (Hologram hjälper forskare att förstå plankton) which highlights the important results of the paper (in Swedish) is now available for public listening.

Vetenskapradion Nyheter airs daily news, reports and in-depth discussions about latest research.

Press release on Tunable critical Casimir forces counteract Casimir-Lifshitz attraction

An illustration of microscopic gold flakes on surface. (Image by F. Schmidt.)
The article Tunable critical Casimir forces counteract Casimir-Lifshitz attraction has been featured in the News of the University of Gothenburg (in English and in Swedish), SISSA-International School of Advanced Studies in Trieste, Italy, Heinrich-Heine-Universität Düsseldorf, and Friedrich-Schiller-Universität Jena.

The study, published in Nature Physics and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, demonstrate that tunable repulsive critical Casimir forces can be used to counteract stiction, i.e., the tendency of tiny parts of micro- and nanoelectromechanical devices to stick together, which is caused by the Casimir-Lifshitz interaction.

The study is featured also in Phys.org, NanoWerk.

Here the links to the press releases:
Casimir vs Casimir – using opposing forces to improve nanotechnology (GU, English)
https://www.gu.se/nyheter/casimir-vs-casimir-klaschande-krafter-kan-forbattra-nanotekniken (GU, Swedish)
Casimir vs Casimir – usare forze opposte per migliorare le nanotecnologie (SISSA, Italian)
Casimir vs Casimir – using opposing forces to improve nanotechnology (SISSA, English)
Nano-Bauteile clever voneinander lösen (Heinrich-Heine-Universität Düsseldorf)
Clever method for separating nano-components (Friedrich-Schiller-Universität Jena)
Clever method for separating nano-components (Phys.org)
Clever method for separating nano-components (NanoWerk)

Tunable critical Casimir forces counteract Casimir-Lifshitz attraction published in Nature Physics

Gold flake suspended over a functionalized gold-coated substrate. (Image by F. Schmidt.)
Tunable critical Casimir forces counteract Casimir-Lifshitz attraction
Falko Schmidt, Agnese Callegari, Abdallah Daddi-Moussa-Ider, Battulga Munkhbat, Ruggero Verre, Timur Shegai, Mikael Käll, Hartmut Löwen, Andrea Gambassi and Giovanni Volpe
Nature Physics (2022)
arXiv: 2202.10926
doi: 10.1038/s41567-022-01795-6

Casimir forces in quantum electrodynamics emerge between microscopic metallic objects because of the confinement of the vacuum electromagnetic fluctuations occurring even at zero temperature. Their generalization at finite temperature and in material media are referred to as Casimir-Lifshitz forces. These forces are typically attractive, leading to the widespread problem of stiction between the metallic parts of micro- and nanodevices. Recently, repulsive Casimir forces have been experimentally realized but their reliance on specialized materials prevents their dynamic control and thus limits their further applicability. Here, we experimentally demonstrate that repulsive critical Casimir forces, which emerge in a critical binary liquid mixture upon approaching the critical temperature, can be used to actively control microscopic and nanoscopic objects with nanometer precision. We demonstrate this by using critical Casimir forces to prevent the stiction caused by the Casimir-Lifshitz forces. We study a microscopic gold flake above a flat gold-coated substrate immersed in a critical mixture. Far from the critical temperature, stiction occurs because of dominant Casimir-Lifshitz forces. Upon approaching the critical temperature, however, we observe the emergence of repulsive critical Casimir forces that are sufficiently strong to counteract stiction. This experimental demonstration can accelerate the development of micro- and nanodevices by preventing stiction as well as providing active control and precise tunability of the forces acting between their constituent parts.

Press release on Microplankton life histories revealed by holographic microscopy and deep learning

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
The article Microplankton life histories revealed by holographic microscopy and deep learning has been featured in the news of University of Gothenburg (in English & Swedish) and in the press release of eLife (in English).

The study, now published in eLife, and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, demonstrates how the combination of holographic microscopy and deep learning provides a strong complimentary tool in marine microbial ecology. The research allows quantitative assessments of microplankton feeding behaviours, and biomass increase throughout the cell cycle from generation to generation.

The study is featured also in eLife digest.

Here are the links to the press releases:
Researchers combine microscopy with AI to characterise marine microbial food web (eLife, English)
Holographic microscopy provides insights into the life of microplankton (GU, English)
Hologram ger insyn i planktonens liv (GU, Swedish)
The secret lives of microbes (eLife digest)

Seminar by G. Volpe at QSIT, ETH Zurich, 3 November 2022

Active droploids. (Image taken from Nat. Commun. 12, 6005 (2021).)
Experimental study of critical fluctuations and critical Casimir forces
Giovanni Volpe
Invited seminar at QSIT/Quantum Center, ETH Zurich
Thursday, November 3, 2022 – 16:00 – 17:00

Critical Casimir forces (CCF) are a powerful tool to control the self-​assembly and complex behavior of microscopic and nanoscopic colloids. While CCF were theoretically predicted in 1978, their first direct experimental evidence was provided only in 2008, using total internal reflection microscopy (TIRM). Since then, these forces have been investigated under various conditions, for example, by varying the properties of the involved surfaces or with moving boundaries. In addition, a number of studies of the phase behavior of colloidal dispersions in a critical mixture indicate critical Casimir forces as candidates for tuning the self-​assembly of nanostructures and quantum dots, while analogous fluctuation-​induced effects have been investigated, for example, at the percolation transition of a chemical sol, in the presence of temperature gradients, and even in granular fluids and active matter. In this presentation, I’ll give an overview of this field with a focus on recent results on the measurement of many-​body forces in critical Casimir forces, the realization of micro-​ and nanoscopic engines powered by critical fluctuations, and the creation of light-​controllable colloidal molecules and active droploids.

Date: Thursday, November 3, 2022
Time: 16:00
Place: ETH Zurich, Campus Hönggerberg, HPF G 6
Host: Lukas Novotny

Microplankton life histories revealed by holographic microscopy and deep learning published in eLife

Tracking of microplankton by holographic optical microscopy and deep learning. (Image by H. Bachimanchi.)
Microplankton life histories revealed by holographic microscopy and deep learning
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe
eLife 11, e79760 (2022)
arXiv: 2202.09046
doi: 10.7554/eLife.79760

The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the oceanic food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates and handling times for individual microplanktons. This method is particularly useful to explore the flux of carbon through micro-zooplankton, the most important and least known group of primary consumers in the global oceans. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.