Presentation by G. Wang at ISMC 2022, Poznan, 20 September 2022

Recognize and selectively trap chiral particles by critical Casimir force. (Image by G. Wang.)
Nanopositioning and nanoalignment of microparticles on patterned surfaces
Gan Wang, Piotr Nowakowski, Nima Farahmand, Benjamin Midtvedt, Falko Schmidt, Mikael Käll, Svyatoslav Kondrat, Sigfried Dietrich and Giovanni Volpe
Date: 20 September 2022
Time: 14:10 (CEST)

Direct manipulation of objects in a solution can provide opportunities to investigate material properties and construct microscopic devices. However, currently available methods, such as optical tweezers and thermal tweezers, have several limitations especially to control the orientation and alignment of particles near surfaces. Here, we experimentally demonstrate that by exploiting the critical Casimir effect, emerging in the presence of a critical binary liquid, microparticles (diameter d≈2µm) can be trapped with nanometer precision. We investigated the motion of SiO2 microscopic disks above nanopatterned surfaces coated with a thin gold film immersed inside a critical mixture. By adjusting the adsorption preference of the gold film to one of the two components of the mixture liquid, we can finely tune the balance between the critical Casimir repulsion and attraction generated between different regions of the substrate and the disk. In this way, we can control the configuration of the disk and make it perform some complex motion. Furthermore, we show how this approach can be used to align particles with patterns, e.g., to sort asymmetric particles with respect to their chirality. We foresee this method can be extended to control the movement of small objects of various materials, thereby severing as a platform to study microscale physical and chemical phenomena.

Keynote Lecture by G. Volpe at ISMC 2022, Poznan, 20 September 2022

An exemplar of Hexbugs, commercially available toy robots that have been used in the experimental demonstration proposed. (Image from arXiv: 2209.04168)
Playing with Active Matter
Giovanni Volpe
Keynote Lecture at ISMC 2022
Poznan, Poland
20 September 2022, 13:30 CEST

In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.

Presentation by J. Pineda at ISMC 2022, Poznan, 19 September 2022

Input graph structure including a redundant number of edges. (Image by J. Pineda.)
Revealing the spatiotemporal fingerprint of microscopic motion using geometric deep learning
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, and Carlo Manzo
Submitted to ISMC 2022
Date: 19 September 2022
Time: 13:40 (CEST)

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.

Presentation by H. Bachimanchi at ISMC 2022, Poznan, 19 September 2022

Plankton tracking with holographic microscope and deep learning. (Image by H. Bachimanchi.)
Quantitative microplankton tracking with holographic microscopy and deep learning
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe
Presentation at ISMC 2022
Poznan, Poland
19 September 2022, 12:40 CEST

A droplet of sea water contains an entire ecosytem. There are microscopic plants, the phytoplanktons, which produce oxygen by absorbing carbon dioxide from the atmsphere by the process of photosynthesis. There are microscopic animals, the microzooplankton, which feed on the phytoplankton. In oceanic ecology, phytoplanktons consume around 65 peta grams of carbon annually, producing approximately 50% of oxygen on the Earth. Microzooplankton take on the role of herbivores, and consume about two thirds (40 Pg carbon) of this primary production. Despite their central importance, our understanding of the phytoplankton and microzooplankton in shaping oceanic communities is still much less developed at a single plankton level.
Here, we demonstrate that by combining holographic microscopy with deep learning, we can follow microplanktons through generations, by continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data, and allow measurements over extended periods of time. 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. We exemplify this by detailed descriptions of microzooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.

Soft Matter Lab members present at ISMC 2022, Poznan, 19-23 September 2022

The Soft Matter Lab participates to the ISMC 2022 in Poznan, Poland, 19-23 September 2022, with the presentations listed below.

Invited Talk by G. Volpe at Fluctuations in small complex systems VI, Venice, 9 September 2022

Label-free measurement of biomolecules and their diffusion
Giovanni Volpe
9 September 2022, 16:45 (CEST)
Venice meeting on Fluctuations in small complex systems VI
Istituto Veneto di Scienze, Lettere ed Arti
Palazzo Franchetti, Venezia, 5-9 September 2022

Presentation by M. Rey at ECIS 2022, Chania, 04 September 2022

Emulsion droplet stabilized by PNIPAM microgels. (Image by M. Rey.)
On the breaking mechanism of temperature-responsive emulsions
Marcel Rey
Submitted to ECIS 2022
Date: 05 September 2022
Time: 16:40 (CET)

Temperature-responsive microgel-stabilized emulsions combine long-term storage with controlled release of the encapsulated liquid upon temperature increase. The destabilisation mechanism was previously primarily attributed to the shrinkage or desorption of the temperature-responsive microgels, leading to a lower surface coverage inducing coalescence.
Here, we link the macroscopic emulsion stability to the thermo-responsive behaviour and microstructure of individual microgels confined at liquid interfaces and demonstrate that the breaking mechanism is fundamentally different to that previously anticipated. Breaking of thermoresponsive emulsions is induced via bridging points in flocculated emulsions, where microgels are adsorbed to two oil droplets. These bridging microgels induce an attractive force onto both interfaces when heated above their volume phase transition temperature, which induces coalescence. Surprisingly, if such bridging points are avoided by low shear emulsification, the obtained emulsion is insensitive to temperature and remains stable even up to 80 °C.

Martin Selin presented his half-time seminar on 2 September 2022

Martin Selin’s half-time seminar: Opponent Dag Hanstorp (left), Martin Selin (right). (Photo by H. P. Tanabalan.)
Martin Selin completed the first half of his doctoral studies and defended his half-time on the 2nd of September 2022.

The presentation was held in hybrid format, with part of the audience in the Von Bahr room and the rest connected through zoom. The half-time consisted of a presentation of Martins two main projects followed by a discussion and questions proposed by Martins opponent Dag Hanstorp.

The presentation started providing a background on optical tweezers and continued with the ongoing project of positioning quantum dots using optical tweezers. Thereafter the presentation continued with the Minitweezers project. Data on DNA stretching was presented and shown to be in good agreement with results found in literature. Lastly the future of the two projects were outlined. Specifically, how to address the challenging task of detecting moving quantum dots and how to improve on the Minitweezers system through automation.

Martin Selin during his half-time seminar. (Photo by L. Natali.)

An anomalous competition: assessment of methods for anomalous diffusion through a community effort

An anomalous competition: assessment of methods for anomalous diffusion through a community effort
Carlo Manzo, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 25 August 2022
Time: 9:00 (PDT)

Deviations from the law of Brownian motion, typically referred to as anomalous diffusion, are ubiquitous in science and associated with non-equilibrium phenomena, flows of energy and information, and transport in living systems. In the last years, the booming of machine learning has boosted the development of new methods to detect and characterize anomalous diffusion from individual trajectories, going beyond classical calculations based on the mean squared displacement. We thus designed the AnDi challenge, an open community effort to objectively assess the performance of conventional and novel methods. We developed a python library for generating simulated datasets according to the most popular theoretical models of diffusion. We evaluated 16 methods over 3 different tasks and 3 different dimensions, involving anomalous exponent inference, model classification, and trajectory segmentation. Our analysis provides the first assessment of methods for anomalous diffusion in a variety of realistic conditions of trajectory length and noise. Furthermore, we compared the prediction provided by these methods for several experimental datasets. The results of this study further highlight the role that anomalous diffusion has in defining the biological function while revealing insight into the current state of the field and providing a benchmark for future developers.

Presenter: Giovanni Volpe

Presentation by Y.-W. Chang at SPIE-ETAI, San Diego, 24 August 2022

Deep-learning-detected tau deposition (color in orange) for Alzheimer’s Disease. (Image by Y.-W. Chang.)
Deep-learning analysis in tau PET for Alzheimer’s continuum
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 16:40 (PDT)

Previous studies have suggested that Alzheimer’s disease (AD) is typically characterized by abnormal accumulation of tau proteins in neurofibrillary tangles. This is usually assessed by measuring tau levels in regions of interest (ROIs) defined based on previous post-mortem studies. However, it remains unclear where this approach is suitable for assessing tau accumulation in vivo across the different stages of individuals. This study employed a data-driven deep learning approach to detect tau deposition across different AD stages at the voxel level. Moreover, the classification performance of this approach on distinguishing different AD stages was compared with the one using conventional ROIs.