Invited talk by G. Volpe at GSJP, 1 October 2020

SLogo of GSJP2020 – First Global Symposium on Janus Particles.

Giovanni Volpe will give an online invited presentation at the First Global Symposium on Janus Particles (GSJP) 2020.

GSJP will bring together a collection of experts who are in the vanguard of scientific and engineering investigations on Janus particles all around the globe.

The contribution of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Light-controlled Assembly of Active Colloidal Molecules

Activity and life have emerged from a primordial broth of simple building blocks when the presence of energy flows made these blocks come together and interact in non-trivial ways. Here, we use experiments and simulations demonstrating that active molecules can be created and controlled by light. Shining light on a primordial broth containing passive particles of two different species, we create active colloidal molecules of increasing complexity, which behave as migrators, spinners and rotators. This demonstrates a powerful new route for nonequilibrium self-assembly, which may help explaining the emergence of complex systems in living matter and may also proof useful as a design principle for the construction of flexible micromotors and cargo transport in health care applications.

Date: 2 October 2020
Time: 10:10 (EST)
Place: Online

Presentation by S. Helgadottir at the Gothenburg Science Festival, 2 October 2020

Logo of the Gothenburg Science Festival.

Saga Helgadottir will give a presentation at the Gothenburg Science Festival 2020.

The International Science Festival Gothenburg is one of Europe’s leading popular science events. Its first edition dates back to 1997, and it is held every year in spring.
This year the festival will take place during autumn, 28 September-4 October. Due to the current situation the festival will be a digital event. The digital festival will be available during the week of the festival.

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

Saga Helgadottir
Deep Learning for Object Recognition
Deep Learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. In this talk, I will show how Deep Learning can be used to identify objects in images, in particular microscopic particles.

Date: 2 October 2020
Time: 18:08
Duration: 17′
Link: Deep Learning for Object Recognition

Links:
Vetenskapsfestivalen Göteborg (in Swedish)
The International Science Festival Gothenburg (in English)
Full Program

Invited talk by G. Volpe at SCOP2020, 25 September 2020

Student Conference on Optics and Photonics (SCOP), organized by the OSA student chapter of the Physical Research Laboratory, Ahmedabad, India.

Giovanni Volpe will give an online invited presentation at the Student Conference on Optics and Photonics (SCOP), organized by the OSA student chapter of Physical Research Laboratory, Ahmedabad, India.

The conference addresses various topics in optics with an emphasis on non linear optics and quantum optics, will be held during 23-25 September, 2020 at the Physical Research Laboratory (PRL), Ahmedabad, India.
The conference includes invited talks by eminent scientists from India and abroad, as well as posters and oral presentations by student participants and research fellows.

The contribution of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Deep Learning for microscopy and optical trapping
Date: 25 September 2020
Time: 15:10 IST (GMT+5:30)
Place: Online

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.

 

E-workshop: Novel Features and Applications of Optical Manipulation

The School of Nano Science, IPM, Tehran, Iran, with the support of IASBS, Zanjan, Iran, is organizing a one-day e-workshop on
Novel Features and Applications of Optical Manipulation
on September 8th, 2020.

The workshop will address the latest features of optical manipulation. Distinguished lecturers in the field will present exciting aspects and applications of optical manipulation along with providing educational outreach to students.

The workshop is open to all and it is free, but pre-registration is required. Registration dates: between 10 and 25 August.

Invited lecturers:
Prof. Kishan Dolakia
Prof. Giovanni Volpe
Prof. Onofrio Maragò
Dr. Valentina Emiliani
Dr. Samaneh Rezvani
Dr. Fatemeh Kalandarifard

Organizers:
Dr. Alireza Moradi
Prof. Reza Asgari

Date: 8 September 2020
Link: Workshop Homepage, Registration

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)

DeepTrack: A comprehensive deep learning framework for digital microscopy

DeepTrack: A comprehensive deep learning framework for digital microscopy
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Daniel Midtvedt, Giovanni Volpe
Click here to see the slides.

Despite the rapid advancement of deep learningmethods for image analysis, they remain under-utilized for the analysis of digital microscopy images. State of the artmethods 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, diicult to reappropriate and almost impossible to interface with other methods. Finally, it is prohibitively difficult to procure representative datasets with corresponding labels. 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 provides a comprehensive programming API for creating representative synthetic data, designed to exactly suit the problem. DeepTrack images samples of refractive index or flourophore distributions using physical simulations of 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. 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. To demonstrate the versatility of the framework,we show a few typical use-cases, including cell-counting in dense biological samples, extracting 3-dimensional tracks from 2-dimensional videos, and distinguishing and tracking microorganisms in bright-field videos.

Poster Session
Time: June 22nd 2020
Place: Twitter and virtual reality

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

Poster Slides

Saga Helgadottir – POM Poster – Page 1
Saga Helgadottir – POM Poster – Page 2
Saga Helgadottir – POM Poster – Page 3
Saga Helgadottir – POM Poster – Page 4

Enhanced force-field calibration via machine learning

Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe

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

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.

Poster Session
Time: June 22nd 2020
Place: Twitter

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

Poster Slides

Aykut Argun – POM Poster – Page 1
Aykut Argun – POM Poster – Page 2
Aykut Argun – POM Poster – Page 3
Aykut Argun – POM Poster – Page 4

Holographic characterisation of subwavelength particles enhanced by deep learning

Holographic characterisation of subwavelength particles enhanced by deep learning
Benjamin Midtvedt, Erik Olsen, Fredrick Eklund, Jan Swenson, Fredrik Höök, Caroline Beck Adiels, Giovanni Volpe and Daniel Midtvedt

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

The characterisation of the physical properties of nanoparticles in their native environment plays a central role in a wide range of fields, from nanoparticle-enhanced drug delivery to environmental nanopollution assessment. Standard optical approaches require long trajectories of nanoparticles dispersed in a medium with known viscosity to characterise their diffusion constant and, thus, their size. However, often only short trajectories are available, while the medium viscosity is unknown, e.g., in most biomedical applications.
In this work, we demonstrate a label-free method to quantify size and refractive index of individual subwavelength particles using two orders of magnitude shorter trajectories than required by standard methods, and without assumptions about the physicochemical properties of the medium. We achieve this by developing a weighted average convolutional neural network to analyse the holographic images of the particles. As a proof of principle, we distinguish and quantify size and refractive index of silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. As an example of an application beyond the state of the art, we demonstrate how this technique can monitor the aggregation of polystyrene nanoparticles, revealing the time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.
This technique opens new possibilities for nanoparticle characterisation with a broad range of applications from biomedicine to environmental monitoring.

Poster Session
Time: June 22nd 2020
Place: Twitter

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

Poster Slides

Daniel Midtvedt – POM Poster – Page 1
Daniel Midtvedt – POM Poster – Page 2
Daniel Midtvedt – POM Poster – Page 3
Daniel Midtvedt – POM Poster – Page 4