CORDIS News article on ComplexSwimmers

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)

CORDIS, the Community Research and Development Information Service of the European Commission, recently covered Giovanni Volpe’s ComplexSwimmers ERC-StG grant in a news:
Throwing down the scientific gauntlet to assess methods for anomalous diffusion.
The article highlights the joint results obtained by three EU-backed research projects (NOQIA, OPTOlogic and ComplexSwimmers) dealing with anomalous diffusion.

Presentation by G. V. P. Kumar, 24 November 2021

Thermoplasmonic Tweezers: Probing single-molecules and more
G. V. Pavan Kumar
IISER, Pune, India.
24 November 2021
Online

In this presentation, we will discuss two specific issues: How to perform single-molecule surface enhanced Raman scattering (SERS) in an optothermal trap? and how to design optothermal fields to trap and interrogate molecules and colloids in a fluid?

In recent years, performing SERS in optical traps has emerged as an important development in nano- and bio-photonics. To this end, tweezer techniques based on surface-plasmons facilitate deeper optical potentials at sub-wavelength scales, and simultaneously provide enhanced electric and optothermal fields. In this
presentation, we will discuss various strategies developed in my laboratory to perform single-molecule SERS in optical and plasmonic tweezer platforms. Specifically, we will highlight some thermoplasmonic effects and directionality aspects of the tweezer platforms in metallic thin film and some plasmonic nano-architectures.

Short bio:

G.V. Pavan Kumar is an associate professor of physics at the Indian Institute of Science Education and Research (IISER), Pune, India.
He obtained his PhD from JNCASR, Bangalore. Subsequently he was a postdoctoral fellow at ICFO-Barcelona and Purdue University, before joining IISER in 2010.
His current research interests are optical, optothermal and nanophotonic forces and their utility in probing single molecules and soft-matter systems at micro and nanoscale.
To this end, his lab has been interfacing optical tweezer platforms with a variety of optical spectroscopy and microscopy tools.
He blogs on topics related to science: https://backscattering.wordpress.com/

Invited Presentation by G. Volpe at FiO LS, 4 November 2021

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)
DeepTrack 2.0: A Framework for Deep Learning for Microscopy
Giovanni Volpe
Invited Presentation at Frontiers in Optics + Laser Science
Online
4 November 2021
4:00 PM

We present DeepTrack 2.0, a software to design, train, and validate deep-learning solutions for digital microscopy. We demonstrate it for applications from particle localization, tracking, and characterization, to cell counting and classification, to virtual staining.

Link: FTh6A.3

Press release on Objective comparison of methods to decode anomalous diffusion

The article Objective comparison of methods to decode anomalous diffusion has been featured in the News of the University of Gothenburg.

The study, published in Nature Communications and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, originates from the AnDi Challenge, a competition co-organised by Giovanni Volpe with researchers from University of Vic – Central University of Catalunya, Institute of Photonic Sciences in Barcelona, University of Potsdam, and Valencia Polytechnic University.

The challenge was held during March–November 2020 and consisted of three main tasks concerning anomalous exponent inference, model classification, and trajectory segmentation. The goal was to provide an objective assessment of the performance of methods to characterise anomalous diffusion from single trajectories.

Here the links to the press releases:
English: A scientific competition led to improved methods for analysing the diffusion of particles.
Swedish: En vetenskaplig tävling ledde till förbättrade metoder för att analysera diffusion av partiklar.

Seminar by D. Midtvedt at Freie Universität Berlin, 29 October 2021

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)
Quantitative digital microscopy enhanced by deep learning
Daniel Midtvedt
(online at) Freie Universität Berlin, Germany
29 October 2021

Video microscopy has a long history of providing insight and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time-consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis.
However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we recently introduced a software, DeepTrack 2.0, to design, train, and validate deep-learning solutions for digital microscopy.
In this talk, I will show how this software can be used in a broad range of applications, from particle localization, tracking, and characterization, to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and thanks to its open-source, object-oriented programing, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Objective comparison of methods to decode anomalous diffusion published in Nature Communications

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás Firbas, Òscar Garibo i Orts, Alessia Gentili, Zihan Huang, Jae-Hyung Jeon, Hélène Kabbech, Yeongjin Kim, Patrycja Kowalek, Diego Krapf, Hanna Loch-Olszewska, Michael A. Lomholt, Jean-Baptiste Masson, Philipp G. Meyer, Seongyu Park, Borja Requena, Ihor Smal, Taegeun Song, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Arthur Widera, Maciej Lewenstein, Ralf Metzler, and Carlo Manzo
Nat. Commun. 12, Article number: 6253 (2021)
doi: 10.1038/s41467-021-26320-w
arXiv: 2105.06766

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Invited Talk by G. Volpe at Microscopies and Spectroscopies: Accessing the Nanoscale, 28 October 2021

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)
Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
Invited Talk at the XXXVI Trobades Cientifíques de la Mediterránia – Josep Miquel Vidal
Microscopies and Spectroscopies: Accessing the Nanoscale
Menorca, Spain
28 October 2021
11:40 AM

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep- learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user- friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Press release on Active Droploids

The article Active Droploids has been featured in a press release of the University of Gothenburg.

The study, published in Nature Communications, examines a special system of colloidal particles and demonstrates a new kind of active matter, which interacts with and modifies its environment. In the long run, the result of the study can be used for drug delivery inside the human body or to perform sensing of environmental pollutants and their clean-up.

Here the links to the press releases:
English: Feedback creates a new class of active biomimetic materials.
Swedish: Feedback möjliggör en ny form av aktiva biomimetiska material.

The article has been features also in Mirage News, Science Daily, Phys.org, Innovations Report, Informationsdienst Wissenschaft (idw) online, Nanowerk.

Presentation by S. Olsson, 20 October 2021

Machine Learning for Molecular dynamics — Why bother?
Simon Olsson
Chalmers University of Technology
20 October 2021
Online

With faster compute-infrastructures, molecular simulations play an increasingly important role in the basic sciences and application areas such as drug and materials design. Simultaneously, machine learning and artificial intelligence are receiving increased attention due to increasing volumes of data generated both inside and outside of science. In this talk, I will talk about a few applications of these technologies in molecular simulation, focusing on biomolecular simulations [1,2]

[1] Olsson & Noé ”Dynamic Graphical Models of Molecular Kinetics” Proc. Natl. Acad. Sci. U.S.A. (2019) doi: 10.1073/pnas.1901692116.
[2] Noe†, Olsson, Köhler, Wu ”Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning” Science (2019). 365, eaaw1147. doi:10.1126/science.aaw1147.

Link: http://www.cse.chalmers.se/~simonols/

Keynote Talk by G. Volpe at CIIBBI, 15 October 2021

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)
Deep Learning for Microscopy with Biomedical Applications
Giovanni Volpe
Keynote Talk at the 2nd International Congress of Biomedical Engineering and Bioengineering
Online
15 October 2021
14:00 CEST

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.