Soft Matter Lab

Optical force field reconstruction using Brownian trajectories

Optical force field reconstruction using Brownian trajectories
Laura Pérez García, Jaime Donlucas Pérez, Giorgio Volpe, Alejandro V. Arzola & Giovanni Volpe

Click here to see the slides.
Twitter Link: here.

Optical tweezers have evolved into sophisticated tools for the measurement and application of nanoscopic forces; its use ranges from mechanobiology to cooling and trapping atoms.
Despite their ever-growing interest, the methods employed to measure optical forces have not changed much in the last 30 years. The key methods measure the potential function, the autocorrelation function (ACF), or the power spectral density (PSD) of an optically trapped particle’s motion. Unfortunately, all these techniques have some drawbacks: they require large amounts of data acquired for long times (potential) or at high frequency (ACF and PSD); they cannot identify non-conservative force-field components; they can only measure the properties of stable equilibrium positions, and they require setting several parameters carefully and expertly [1]. These shortcomings have limited the possibility of measuring nanoscopic forces in many potential applications, such as experiments with non-conservative force fields and out-of-equilibrium conditions.

We have recently introduced a simple, robust, and fast algorithm that permits to reconstruct microscopic force fields from Brownian trajectories, Force Reconstruction via Maximum-likelihood-estimator Analysis — FORMA. FORMA exploits the fact that in the proximity of an equilibrium position, the force field can be approximated by a linear form, and therefore, optimally estimated using a linear maximum-likelihood-estimator. Its key advantages are that FORMA does not require setting analysis parameters, it executes orders-of-magnitude faster than other more standard methods, and it requires ten times fewer data to achieve the same precision and accuracy. Finally, FORMA also permits the characterization of non-conservative force fields and non-stable equilibrium positions in extended force fields [2].

References:

[1] Jones et al. Optical tweezers: Principles and applications. Cambridge, 2015.
[2] L. Pérez García, et al. Nat. Commun. 9, 5166 (2018).

Poster Session
Time: June 22nd 2020
Place: Twitter

POM Conference
Link: POM
Time: June 25th 2020
Place: Online

Poster Slides

Laura Pérez García – POM Poster – Page 1

Laura Pérez García – POM Poster – Page 2

Laura Pérez García – POM Poster – Page 3

Laura Pérez García – POM Poster – Page 4

Controlling the dynamics of colloidal particles by critical Casimir forces

Controlling the dynamics of colloidal particles by critical Casimir forces
Alessandro Magazzù, Agnese Callegari, Juan Pablo Staforelli, Andrea Gambassi, Siegfried Dietrich and Giovanni Volpe

Click here to see the slides.
Twitter Link: here.

Critical Casimir forces can play an important role for applications in nano-science and nano-technology, owing to their piconewton strength, nanometric action range, fine tunability as a function of temperature, and exquisite dependence on the surface properties of the involved objects. Here, we investigate the effects of critical Casimir forces on the free dynamics of a pair of colloidal particles dispersed in the bulk of a near-critical binary liquid solvent, using blinking optical tweezers. In particular, we measure the time evolution of the distance between the two colloids to determine their relative diffusion and drift velocity. Furthermore, we show how critical Casimir forces change the dynamic properties of this two-colloid system by studying the temperature dependence of the distribution of the so-called first-passage time, i.e., of the time necessary for the particles to reach for the first time a certain separation, starting from an initially assigned one. These data are in good agreement with theoretical results obtained from Monte Carlo simulations and Langevin dynamics.

Poster Session
Time: June 22nd 2020
Place: Twitter

POM Conference
Link: POM
Time: June 25th 2020
Place: Online

Poster Slides

Alessandro Magazzù – POM Poster – Page 1

Alessandro Magazzù – POM Poster – Page 2

Alessandro Magazzù – POM Poster – Page 3

Alessandro Magazzù – POM Poster – Page 4

Dynamics of an active nanoparticle in an optical trap

Dynamics of an active nanoparticle in an optical trap
Falko Schmidt, Hana Šípová-Jungová, Mikael Käll, Alois Würger, Giovanni Volpe

Click here to see the slides.
Twitter Link: here.

Active matter systems in non-equilibrium conditions have recently gained great interest from many disciplines such as micro and nanomachines and in living organisms. Probing the dynamics of active Brownian particles (ABPs) under confinement such as found in biological systems gives insight into their non-equilibrium processes. Although previous studies [1-4] have shown the effect of confinement on ABPs on the microscale and macroscale investigating dynamics on the nanoscale remains challenging where thermal fluctuations typically prevail. Here, we are investigating experimentally and theoretically a nanoscopic particle in the harmonic potential of an optical trap and driven away from equilibrium by self-induced concentration gradients. We find that a nanoparticle performs fast orbital rotation at finite distance from the trap center and its probability density shifts from a Gaussian to a skewed distribution. Furthermore, we show that by transfer of spin angular momentum from the trapping beam the direction of the particle’s rotation can be controlled. We develop a theoretical model of this system which reveals that the driving mechanism of such fast rotation is the particle’s non-sphericity providing insight for the development of future nanoscopic engines.

References

[1] S. C. Takatori et al., Nat. Comm., 7, 10694 (2016)
[2] O. Dauchot & V. Démery, Phys. Rev. Lett., 122, 068002 (2019)
[3] A. Pototsky & H. Stark, EPL, 98, 5004 (2012)
[4] F. Schmidt et al., Phys. Rev. Lett., 120, 068004 (2018)

Poster Session
Time: June 22nd 2020
Place: Twitter

POM Conference
Link: POM
Time: June 25th 2020
Place: Online

Poster Slides

Soft Matter Lab presentations at the Photonics Online Meet-up, 22 June 2020

Six members of the Soft Matter Lab (Aykut Argun, Falko Schmidt, Laura Pérez-Garcia, Saga Helgadottir, Alessandro Magazzù, Daniel Midtvedt) were selected for poster presentations at the Photonics Online Meet-up (POM).

POM is an entirely free virtual conference. It aims to bring together a community of early career and established researchers from universities, industry, and government working in optics and photonics.

The meeting, at its second edition, will be held on June 25th 2020, 9-14.30 Central European Time. The virtual poster session will take place on June 22nd, on Twitter and virtual reality.

The poster contributions being presented are:

Aykut Argun
Enhanced force-field calibration via machine learning
Twitter Link: here.

Falko Schmidt
Dynamics of an active nanoparticle in an optical trap
Twitter Link: here.

Laura Pérez-García
Optical force field reconstruction using Brownian trajectories
Twitter Link: here.

Saga Helgadottir
DeepTrack: A comprehensive deep learning framework for digital microscopy
Twitter Link: here.

Alessandro Magazzù
Controlling the dynamics of colloidal particles by critical Casimir forces
Twitter Link: here.

Daniel Midtvedt
Holographic characterisation of subwavelength particles enhanced by deep learning
Twitter Link: here.

Link: Photonics Online Meet-up (POM)

Seminar by G. Volpe at ICFO, 16 June 2020

Lucky Encounters: From Optical Tweezers to deep Learning
Giovanni Volpe
ICFO Alumni Seminar (Online)
16 June 2020

In this semi-autobiographical talk, I will look back at my career and its evolution. It all started at ICFO with a PhD on optical tweezers in 2008. It then continued with a series of diverse research projects on different fields: active matter, stochastic thermodynamics, neurosciences and, finally, deep learning. I will emphasize how my career has been shaped by lucky encounters. Encounters that have taken me to places and topics I’d never have imagined beforehand. But it all makes sense, in insight.

Date: 16 June 2020
Time: 15:00
Place: Online

Benjamin Midtvedt defended his Master Thesis on June 15, 2020. Congrats!

Benjamin Midtvedt defended his Master Thesis in Engineering Mathematics and Computer Science at Chalmers University of Technology on 15 June 2020. Congrats!

Screenshot of Benjamin Midtvedt’s Master Thesis defence.

Title: DeepTrack: A comprehensive deep learning framework for digital microscopy

Despite the rapid advancement of deep-learning methods for image analysis, they remain underutilized for the analysis of microscopy images. State of the art methods require expertise in deep-learning to implement, disconnecting the development of new methods from end-users. The packages that are available are typically highly specialized, challenging to reappropriate, and almost impossible to interface with other methods. Finally, training deep-learning models often requires large datasets of manually annotated images, making it prohibitively difficult to procure training data that accurately represents the problem.

DeepTrack is a deep-learning framework targeting optical microscopy, designed to account for each of these issues. Firstly, it is packaged with an easy-to-use graphical user interface, solving standard microscopy problems with no required programming experience. Secondly, it bypasses the need for manually annotated experimental data by providing a comprehensive programming API for creating representative synthetic data, designed to exactly suit the problem. DeepTrack creates physical simulations of samples described by refractive index or fluorophore distributions, using fully customizable optical systems. To accurately represent the data to be analyzed, DeepTrack supports arbitrary optical aberration and experimental noise. Thirdly, many standard deep-learning methods are packaged with DeepTrack, including architectures such as U-NET, and regularization techniques such as augmentations, decreasing the barrier to entry. Finally, the framework is fully modular and easily extendable to implement new methods, providing both longevity and a centralized foundation to deploy new deep-learning solutions.

We demonstrate the versatility of DeepTrack by training networks to solve a broad range of common microscopy problems, including particle tracking, cell-counting in dense biological samples, multi-particle 3-dimensional tracking, and cell segmentation and classification.

Master Programme: Engineering Mathematics and Computer Science
Supervisor: Giovanni Volpe
Examiner: Giovanni Volpe
Opponents: Aykut Argun and Saga Helgadóttir

Time: 15 June 2020, 16:00
Place: Online via Zoom

Tobias Sandström and Lars Jansson defended their Master Thesis on 15 June, 2020. Congrats!

Tobias Sandström and Lars Jansson defended their Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 15 June 2020. Congrats!

Screenshot of Tobias Sandström and Lars Jansson’s Master Thesis defence.

Title: Graph Convolutional Neural Networks for Brain Connectivity Analysis

We explore the strengths and limitations of Graph Convolutional Neural Networks (GCNs) for classification of graph structured data. GCNs differs from regular Artificial Neural Networks (ANNs) in that they operate directly on graph structures by defining convolutional operators in a non-euclidean space. We show that GCNs perform well on graph structured data, where regular ANNs typically fail due to the arbitrary ordering of nodes. Different GCN architectures are examined and compared to simplistic ANNs. Tests are initially performed on simulated data sets with implicit class-dissimilarities in regards to graph structures. We demonstrate that GCNs is vital in accurately classifying the simulated data. Network performance is later evaluated on structured MRI-data, displaying cortical thicknesses for 68 regions in the brain of patients with Alzheimer’s disease and a healthy control group. On the structured MRI-data, both GCNs and regular ANNs are shown to be able classifiers. However, it is crucial for the performance of ANNs that an order of nodes can be imposed on the MRI-data from labeled brain regions.

Supervisors: Jonas Andersson & Alice Deimante Neimantaite, Syntronic AB
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Jonathan Bergqvist

Time: 15 June, 2020, 14:00
Place: Online via Zoom

Presentation by S. Helgadottir at SAIS Workshop, 17 June 2020

Saga Helgadottir will give a presentation at the 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS), that will be held as an online conference on June 16 – 17, 2020.

The SAIS workshop is a forum for building the Swedish AI research community and nurture networks across academia and industry. Because of the concern for the COVID-19, the workshop this year is an online conference.

The contributions of Saga Helgadottir will be presented according to the following schedule:

Saga Helgadottir
Medical Diagnosis with Machine Learning
Date: 17 June 2020
Time: 15:00 CEST

Link: SAIS Workshop 2020 program

Hillevi Wachtmeister defended her Master Thesis on June 11, 2020. Congrats!

Hillevi Wachtmeister defend her Master Thesis in Physics at Chalmers University of Technology on 11 June 2020. Congrats!

Screenshot of Hillevi Wachtmeister’s Master Thesis defence.

Title: Tracking marine micro organisms using deep learning

The goal of this project is to develop a software that can be used to study swimming patterns of marine micro organisms. The software is based on a neural network, which is trained to recognize different types of plankton. The predictions from the network are then used to find the positions of the plankton, and then track their movements.

The project is divided into two parts. First, videos containing only one type of plankton, Lingulodinium polyedra and Alexandrium tamarense respectively, are analyzed. A type of neural network, called U-net, is trained to segment the input images into background and plankton sections. From the segmented images, positions can be obtained and then connected to form a trajectory for each plankton. The drift of the plankton movements is calculated and subtracted from the trajectories, and finally the speed and net displacement is calculated. The results from the single plankton experiments are compared to a previous analysis that was made using the algorithmic method TrackMate.

Secondly, videos containing two types of plankton are analyzed. Two experiments are conducted using Strombidium arenicola and Rhodomonas baltica in the first experiment, and Alexandrium tamarense and Rhodomonas baltica in the second. The segmented images, obtained from the U-net, consists of an additional plankton section for the second type of plankton present in the experiment.

The analysis of the single plankton experiments yields longer and fewer trajectories using the U-net method, compared to the previous TrackMate results. This shows that the TrackMate method is losing plankton at more positions, compared to the U-net method. The U-net method is therefore able to track each plankton for a longer time. The multi-plankton experiments proves the network’s ability to distinguish and track multiple plankton at the same time.

Master programme: MPPHYS – Physics
Supervisor: Daniel Midtvedt
Examiner: Giovanni Volpe
Opponent: Frida Eriksson

Date: 11 June 2020, 9:00
Place: Nexus + Online via Zoom

Anisotropic dynamics of a self-assembled colloidal chain in an active bath published on Soft Matter

Bright-field microscopy image of a magnetic chain trapped at the liquid-air interface in a bacterial bath

Anisotropic dynamics of a self-assembled colloidal chain in an active bath
Mehdi Shafiei Aporvari, Mustafa Utkur, Emine Ulku Saritas, Giovanni Volpe & Joakim Stenhammar
Soft Matter, 2020, 16, 5609-5614
doi: https://doi.org/10.1039/D0SM00318B
arXiv: 2002.09961

Anisotropic macromolecules exposed to non-equilibrium (active) noise are very common in biological systems, and an accurate understanding of their anisotropic dynamics is therefore crucial. Here, we experimentally investigate the dynamics of isolated chains assembled from magnetic microparticles at a liquid–air interface and moving in an active bath consisting of motile E. coli bacteria. We investigate both the internal chain dynamics and the anisotropic center-of-mass dynamics through particle tracking. We find that both the internal and center-of-mass dynamics are greatly enhanced compared to the passive case, i.e., a system without bacteria, and that the center-of-mass diffusion coefficient D features a non-monotonic dependence as a function of the chain length. Furthermore, our results show that the relationship between the components of D parallel and perpendicular with respect to the direction of the applied magnetic field is preserved in the active bath compared to the passive case, with a higher diffusion in the parallel direction, in contrast to previous findings in the literature. We argue that this qualitative difference is due to subtle differences in the experimental geometry and conditions and the relative roles played by long-range hydrodynamic interactions and short-range collisions.