Invited Talk by G. Volpe at ICTP, 8 September 2021

Neural net with input layer (left), dense internal layers, and output layer (right). (Image from the article Machine Learning for Active Matter)
Machine Learning for Active Matter: Opportunities and Challenges

Giovanni Volpe
Invited Talk
(online at) ICTP, Trieste, Italy
8 September 2021, 11:30 CEST

Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis; providing new data-driven models; improving navigation and search strategies; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.

Links:
Conference: Statistical Physics of Complex Systems | (smr 3624)
Program of Day 1 (8 September)

The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus published in Science Advances

M. xanthus cell-cell and cell-particle local interactions during cellular aggregation.
The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus
Karla C. Hernández Ramos, Edna Rodríguez-Sánchez, Juan Antonio Arias del Angel, Alejandro V. Arzola, Mariana Benítez, Ana E. Escalante, Alessio Franci, Giovanni Volpe, Natsuko Rivera-Yoshida
Sci. Adv. 7(35), eabh2278 (2021)
bioRxiv: 10.1101/2021.01.27.428527
doi: 10.1126/sciadv.abh2278

The social soil-dwelling bacteria Myxococcus xanthus can form multicellular structures, known as fruiting bodies. Experiments in homogeneous environments have shown that this process is affected by the physico-chemical properties of the substrate, but they have largely neglected the role of complex topographies. We experimentally demonstrate that the topography alters single-cell motility and multicellular organization in M. xanthus. In topographies realized by randomly placing silica particles over agar plates, we observe that the cells’ interaction with particles drastically modifies the dynamics of cellular aggregation, leading to changes in the number, size and shape of the fruiting bodies, and even to arresting their formation in certain conditions. We further explore this type of cell-particle interaction in a minimal computational model. These results provide fundamental insights into how the environment topography influences the emergence of complex multicellular structures from single cells, which is a fundamental problem of biological, ecological and medical relevance.

The Cognitive Connectome in Healthy Aging published in Frontiers in Aging Neuroscience

Age-independent cognitive connectome in the whole cohort.
The Cognitive Connectome in Healthy Aging
Eloy Garcia-Cabello, Lissett Gonzalez-Burgos, Joana B. Pereira, Juan Andres Hernández-Cabrera, Eric Westman, Giovanni Volpe, José Barroso, & Daniel Ferreira
Front. Aging Neurosci. 13, 530 (2021)
doi: 10.3389/fnagi.2021.694254

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.

Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.

Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.

Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

Extracting quantitative biological information from brightfield cell images using deep learning featured in AIP SciLight

The article Extracting quantitative biological information from brightfield cell images using deep learning
has been featured in: “Staining Cells Virtually Offers Alterative Approach to Chemical Dyes”, AIP SciLight (July 23, 2021).

Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.

Scilight is published weekly (52 issues per year) by AIP Publishing.

Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces published in Nature Nanotechnology

Metavehicles.
Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces
Daniel Andrén, Denis G. Baranov, Steven Jones, Giovanni Volpe, Ruggero Verre, Mikael Käll
Nat. Nanotechnol. (2021)
doi: 10.1038/s41565-021-00941-0
arXiv: 2012.10205

Nanostructured dielectric metasurfaces offer unprecedented opportunities to manipulate light by imprinting an arbitrary phase gradient on an impinging wavefront. This has resulted in the realization of a range of flat analogues to classical optical components, such as lenses, waveplates and axicons. However, the change in linear and angular optical momentum associated with phase manipulation also results in previously unexploited forces and torques that act on the metasurface itself. Here we show that these optomechanical effects can be utilized to construct optical metavehicles – microscopic particles that can travel long distances under low-intensity plane-wave illumination while being steered by the polarization of the incident light. We demonstrate movement in complex patterns, self-correcting motion and an application as transport vehicles for microscopic cargoes, which include unicellular organisms. The abundance of possible optical metasurfaces attests to the prospect of developing a wide variety of metavehicles with specialized functional behaviours.

Extracting quantitative biological information from brightfield cell images using deep learning published in Biophysics Reviews

Virtually-stained generated image for lipid-droplet.
Extracting quantitative biological information from brightfield cell images using deep learning
Saga Helgadottir, Benjamin Midtvedt, Jesús Pineda, Alan Sabirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe
Biophysics Rev. 2, 031401 (2021)
arXiv: 2012.12986
doi: 10.1063/5.0044782

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.

Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis on ChemRxiv

Optical beam focused into the liquid: the tire particles are pushed away from the laser focus.
Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Brente Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
ChemRxiv: https://doi.org/10.33774/chemrxiv-2021-h59n1

Tire and Road Wear Particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ~ 5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of < 5 μm MPs. Here we show that Raman Tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TWRPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman Tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.

Colloquium by G. Volpe at TU-Darmstadt, 18 June 2021, Online

Deep learning for particle tracking. (Image by Aykut Argun)
Deep learning for microscopy, optical trapping, and active matter

Giovanni Volpe
Colloquium
(online at) TU-Darmstadt, Germany
18 June 2021, 14:00 CEST

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter.
In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles.
Finally, I will provide an outlook for the application of deep learning in photonics and active matter.

Olle Fager defended his Master thesis on 15 June 2021. Congrats!

Olle Fager defended his Master thesis in MPCAS at the Chalmers University of Technology on 15 June 2021. Congrats!

Title: Real-Time Multi-Object Tracking and Segmentation with Generated Data using 3D-modelling

Multi-Object Tracking and Segmentation (MOTS) is an important branch of computer vision that has applications in many different areas. In recent developments these methods have been able to reach favorable speed-accuracy trade-offs, making them interesting for real-time applications. In this work different deep learning based MOTS methods have been investigated with the purpose of extending the DeepTrack framework with real-time MOTS capabilities. Deep learning methods rely heavily on the data on which they are trained. The collection and annotation of the data can however be very time-consuming. Therefor, a pipeline is developed and investigated that automatically produces synthetic data by utilizing 3D-modelling. The most accurate tracker achieves a MOTSA score of 94 and the tracker with the best speed-accuracy trade-off achieves a MOTSA score of 88. It is also observed that satisfactory results can be achieved in most situations with a quite general data generation pipeline, indicating that the developed pipeline could be used in different scenarios.

​Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Arianit Zeqiri and Morad Mahmoudyan

Place: Online via Zoom
Time: 15 June, 2021, 16:00

New PhD position in Physics at Soft Matter Lab: last application day 29 June 2021

Soft Matter Lab is looking for motivated candidates for a new PhD position announced on Tuesday, 8 June 2021.

Last application day: Tuesday, 29 June 2021.
Expected employment starting date: 1 September 2021.

Apply here!    [In English]   [In Swedish]

Excerpt from the announcement

Job assignments:
    Possibility 1: Work at the development of DeepTrack 2.0 (https://aip.scitation.org/doi/10.1063/5.0034891 and https://github.com/softmatterlab/DeepTrack-2.0) in close cooperation with the existing developers’ team at Soft Matter Lab. DeepTrack 2.0 is a software framework we are developing to perform quantitative digital video microscopy using deep learning. The PhD student will actively contribute to the technical design and implementation of the components of DeepTrack. Precisely, the task involves the development, testing and application of optical simulation pipelines for training deep learning networks, as well as the development of collaborative projects with other groups interested in using DeepTrack.
    Possibility 2: Build and operate small robots to study swarm robotics with embedded intelligence. The task involves all stages of design, fabrication, testing and programming the robots for different experiments. The robot models (inspired by the Kilobots https://ssr.seas.harvard.edu/kilobots) are small programmable units capable of motion, short-range communications, neighbor detection and more.

Appointment procedure
Please apply online
The application shall include:
– Cover letter with an explanation of why you apply for the position
– CV including scientific publications
– Copy of exam certificate
– Two referees (name, telephone number, relation)

For the required Qualifications, Eligibility, Assessment criteria, Employment, see the link to the announcement below.

Links:
English: PhD Student in Physics (with focus on machine learning and robotics, Soft Matter Lab)
Swedish: Doktorandplats i Fysik (med fokus på maskininlärning och robotik, Soft Matter Lab)

For further information regarding the position
Giovanni Volpe, Professor, 031-786 9137, giovanni.volpe@physics.gu.se