Feedback-controlled active brownian colloids with space-dependent rotational dynamics published in Nature Communications

Active Colloids with Position-Dependent Rotational Diffusivity

Active Colloids with Position-Dependent Rotational Diffusivity
Miguel Angel Fernandez-Rodriguez, Fabio Grillo, Laura Alvarez, Marco Rathlef, Ivo Buttinoni, Giovanni Volpe & Lucio Isa
Nature Communications, 11, 4223 (2020)
doi: 10.1038/s41467-020-17864-4
arXiv: 1911.02291

The non-thermal nature of self-propelling colloids offers new insights into non-equilibrium physics. The central mathematical model to describe their trajectories is active Brownian motion, where a particle moves with a constant speed, while randomly changing direction due to rotational diffusion. While several feedback strategies exist to achieve position-dependent velocity, the possibility of spatial and temporal control over rotational diffusion, which is inherently dictated by thermal fluctuations, remains untapped. Here, we decouple rotational diffusion from thermal fluctuations. Using external magnetic fields and discrete-time feedback loops, we tune the rotational diffusivity of active colloids above and below its thermal value at will and explore a rich range of phenomena including anomalous diffusion, directed transport, and localization. These findings add a new dimension to the control of active matter, with implications for a broad range of disciplines, from optimal transport to smart materials.

Soft Matter Lab presentations at the SPIE Optics+Photonics Digital Forum

Seven members of the Soft Matter Lab (Saga HelgadottirBenjamin Midtvedt, Aykut Argun, Laura Pérez-GarciaDaniel MidtvedtHarshith BachimanchiEmiliano Gómez) were selected for oral and poster presentations at the SPIE Optics+Photonics Digital Forum, August 24-28, 2020.

The SPIE digital forum is a free, online only event.
The registration for the Digital Forum includes access to all presentations and proceedings.

The Soft Matter Lab contributions are part of the SPIE Nanoscience + Engineering conferences, namely the conference on Emerging Topics in Artificial Intelligence 2020 and the conference on Optical Trapping and Optical Micromanipulation XVII.

The contributions being presented are listed below, including also the presentations co-authored by Giovanni Volpe.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Emerging Topics in Artificial Intelligence 2020

Saga Helgadottir
Digital video microscopy with deep learning (Invited Paper)
26 August 2020, 10:30 AM
SPIE Link: here.

Aykut Argun
Calibration of force fields using recurrent neural networks
26 August 2020, 8:30 AM
SPIE Link: here.

Laura Pérez-García
Deep-learning enhanced light-sheet microscopy
25 August 2020, 9:10 AM
SPIE Link: here.

Daniel Midtvedt
Holographic characterization of subwavelength particles enhanced by deep learning
24 August 2020, 2:40 PM
SPIE Link: here.

Benjamin Midtvedt
DeepTrack: A comprehensive deep learning framework for digital microscopy
26 August 2020, 11:40 AM
SPIE Link: here.

Gorka Muñoz-Gil
The anomalous diffusion challenge: Single trajectory characterisation as a competition
26 August 2020, 12:00 PM
SPIE Link: here.

Meera Srikrishna
Brain tissue segmentation using U-Nets in cranial CT scans
25 August 2020, 2:00 PM
SPIE Link: here.

Juan S. Sierra
Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
26 August 2020, 11:50 AM
SPIE Link: here.

Harshith Bachimanchi
Digital holographic microscopy driven by deep learning: A study on marine planktons (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Emiliano Gómez
BRAPH 2.0: Software for the analysis of brain connectivity with graph theory (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Optical Trapping and Optical Micromanipulation XVII

Laura Pérez-García
Reconstructing complex force fields with optical tweezers
24 August 2020, 5:00 PM
SPIE Link: here.

Alejandro V. Arzola
Direct visualization of the spin-orbit angular momentum conversion in optical trapping
25 August 2020, 10:40 AM
SPIE Link: here.

Isaac Lenton
Illuminating the complex behaviour of particles in optical traps with machine learning
26 August 2020, 9:10 AM
SPIE Link: here.

Fatemeh Kalantarifard
Optical trapping of microparticles and yeast cells at ultra-low intensity by intracavity nonlinear feedback forces
24 August 2020, 11:10 AM
SPIE Link: here.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Digital video microscopy with deep learning

Digital video microscopy with deep learning
Saga Helgadottir
(Invited paper)

Microscopic particle tracking has had a long history of providing insight and breakthroughs within the physical and biological sciences, starting with Jean Perrin proved the existens of atoms in 1910 by projecting images of microscopic colloidal particles onto a sheet of paper and manually tracking their displacements. From the start of digital video microscopy over 20 years ago, automated single particle tracking algorithms have followed a similar pattern: pre-processing of the image to reduce noise, segmentation of the image to identify the features of interest, refining of these feature coordinates to sub-pixel accuracy and linking of the feature coordinates over several images to construct particle trajectories. By fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle in good imaging conditions. However, their performance degrades severely at unsteady imaging conditions.
To overcome the limitations of traditional algorithmic approaches, data-driven methods using deep learning have been introduced. Deep-learning algorithms based on convolutional neural networks have been shown to accurately localize holographic colloidal particles and fluorescent biological objects. We have recently developed DeepTrack, a software package based on a convolutional neural network that outperforms algorithmic approaches in tracking colloidal particles as well as non spherical biological objects, especially in the presence of noise and under poor illumination conditions.
In this talk I will give an overview of the history of particle tracking before explaining the details of our solution DeepTrack and finally give an outlook on the field of deep learning in microscopy.

Time and place: Presentation published online on 24 August 2020
SPIE Link: here.

BRAPH 2.0 : Upgrade to a graph theory software for the analysis of brain connectivity

BRAPH 2.0 : Upgrade to a graph theory software for the analysis of brain connectivity
Emiliano Gomez Ruiz, Anna Canal Garcia, Mite Mijalkov, Joana B. Pereira, Giovanni Volpe

There is increasing evidence showing that graph theory is a promising tool to study the human brain connectome. By representing brain regions and their connections as nodes and edges, it allows assessing properties that reflect how well brain networks are organized and how they become disrupted in neurological diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, schizophrenia, multiple sclerosis and autism. Here, we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0), which is a major update of the first object-oriented open source software written in Matlab for graph-theoretical analysis that also implements a graphical interface (GUI). BRAPH utilizes the capability of object-oriented programming paradigm to provide clear, robust, clean, modular, maintainable, and testable code.

Time: 24 August 2020
Place: Online
SPIE Link: here.


Machine learning reveals complex behaviours in optically trapped particles accepted in Machine Learning: Science and Technology

Illustration of a fully connected neural network with three inputs, three outputs, and three hidden layers.

Machine learning reveals complex behaviours in optically trapped particles
Isaac C. D. Lenton, Giovanni Volpe, Alexander B. Stilgoe, Timo A. Nieminen & Halina Rubinsztein-Dunlop
Machine Learning: Science and Technology, 2020, Accepted
doi: 10.1088/2632-2153/abae76
arXiv: 2004.08264

Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can be trained to rapidly and accurately predict the optical forces acting on a microscopic particle. We demonstrate the utility of this approach on two phenomena that are prohibitively slow to accurately simulate otherwise: the escape dynamics of swelling microparticles in an optical trap, and the rotation rates of particles in a superposition of beams with opposite orbital angular momenta. Thanks to its high speed and accuracy, this method can greatly enhance the range of phenomena that can be efficiently simulated and studied.

Optical trapping and critical Casimir forces on ArXiv

Measuring the dynamics of colloids interacting with critical Casimir interaction via blinking optical tweezers: graphical representation of the optical traps.

Optical trapping and critical Casimir forces
Agnese Callegari, Alessandro Magazzù, Andrea Gambassi & Giovanni Volpe
arXiv: 2008.01537

Critical Casimir forces emerge between objects, such as colloidal particles, whenever their surfaces spatially confine the fluctuations of the order parameter of a critical liquid used as a solvent. These forces act at short but microscopically large distances between these objects, reaching often hundreds of nanometers. Keeping colloids at such distances is a major experimental challenge, which can be addressed by the means of optical tweezers. Here, we review how optical tweezers have been successfully used to quantitatively study critical Casimir forces acting on particles in suspensions. As we will see, the use of optical tweezers to experimentally study critical Casimir forces can play a crucial role in developing nano-technologies, representing an innovative way to realize self-assembled devices at the nano- and microscale.