Category: Presentation

Presentation by G. Volpe at NINa Digital Symposium, 11 May 2021

Deep learning for particle tracking. (Image by Aykut Argun.)
Photonics, Brain Connectivity, Deep Learning, and Entrepreneurship at GU Physics
Giovanni Volpe
Seminar
NINa Digital Symposium
11 May 2021, 13:40 CEST

The Soft Matter Lab at Gothenburg University focuses on research at the nexus between photonics, brain connectivity and deep learning. In this presentation, I’ll briefly show our activities along these research directions that can be most interesting for an industry-academia partnership. These include: (1) The development of tools for quantitative digital microscopy enhanced by deep learning, in particular with the recent launch of the Python-based software platform DeepTrack 2.0. (2) The development of tools for the study of brain connectivity, especially within the context of the development of diagnostic and therapeutic tools for neurodegenerative diseases, in particular with the upcoming launch of the Matlab-based software platform Braph 2.0. (3) The development of tools of tools bridging photonics and machine learning. Finally, I’ll briefly present our new startup companies Lucerio Bio and IFLAI.

Presentation by H. Bachimanchi at M2C2, Weizmann Institute, Israel, 5 May 2021

Classification of phytoplankton (blue) and microzooplankton (orange) by holography + deep learning: Schematic of the experimental setup (left). (Image by Harshith Bachimanchi.)
Microzooplankton classification and their feeding patterns by digital holographic microscopy and deep learning
Harshith Bachimanchi
Presentation at Marine Microbial Chemical Communication (M2C2) webinar series
(online) at Weizmann institute of science, Israel
5 May 2021, 15:45 CEST

Phytoplankton and zooplankton are the foundation of the marine food chain. Being an autotrophic primary producer, phytoplankton can generate their own source of energy through photosynthesis. During this process, phytoplankton populations all over the world absorb about 65 Gt (gigatons) of carbon from the atmosphere and thereby equivalently produce the largest amount of oxygen on the earth. The main consumers of this absorbed carbon are the heterotrophic microzooplankton, occupying the next level in the hierarchy of the marine food chain, consuming about two-thirds of the total production (39 Gt). This is likely the largest transition of biological carbon on Earth. Despite being fundamental for our understanding of the carbon cycle and the earth’s climate, the standard estimates leave many questions unanswered at a single microplankton level. Here, we demonstrate that machine learning can be used to estimate the amount of carbon consumed at a single plankton level. We use digital holographic microscopy powered by deep learning to classify planktons by their species and track the biomass of the plankton during individual feeding events. We use the planktonic species, Dunaliella tertiolecta, and Oxyrrhis marina, for our experiments which belong to classes of phytoplankton and microzooplankton respectively. With the help of artificial neural networks, we manage to estimate the carbon consumption and native carbon content at an individual microzooplankton level. Furthermore, we demonstrate the advantages of the approach and compare the results with standard ensemble estimates.

Seminar by G. Volpe at IPCF, Messina for Appunti di Fisica, 6 May 2021, Online

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)
Machine Learning against Epidemics
Giovanni Volpe
Seminar
Appunti di Fisica ’21
(online at) IPCF-Messina, Italy
6 May 2021, 16:00 CET

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these predictions, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Link: Machine Learning against Epidemics, seminar for Appunti di Fisica ’21

Colloquium by G. Volpe at NTNU, 23 April 2021, Online

Deep learning for particle tracking. (Image by Aykut Argun)
Quantitative Digital Microscopy with Deep Learning

Giovanni Volpe
Colloquium
(online at) NTNU, Norway
23 April 2021, 14:15 CET

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.

Link: Quantitative Digital Microscopy with Deep Learning (NTNU)

Soft Matter Lab’s presentations at OSA-OMA 2021

The Soft Matter Lab is involved in six presentations at the OSA Biophotonic Congress: Optics in the Life Sciences 2021, topical meeting of Optical Manipulation and its Applications.
Moreover, three of the presentations were selected as finalists for the best student paper in the topical meeting of Optical Manipulation and its Applications.

You can find the details below:

12 April

15 April

16 April

  • 16:15 CEST
    Calibration of Force Fields Using Recurrent Neural Networks (AF2D.4)
    Aykut Argun, University of Gothenburg

Invited talk by G. Volpe at 11th Nordic Workshop on Statistical Physics, 15 April 2021, Online

Deep learning for particle tracking. (Image by Aykut Argun)
Machine Learning for Active Matter: Opportunities and Challenges

Giovanni Volpe
(online at) Nordita, Stockholm, Sweden
15 April 2021, 14:30-15.25

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.

Date: 15 April 2021
Time: 14:30-15:25
Contribution: Machine Learning for Active Matter: Opportunities and Challenges
Event: 11th Nordic Workshop on Statistical Physics: Biological, Complex, and Non-equilibrium Systems

Presentation by L. Pérez García at OSA-OMA-2021

FORMA allows to identify and characterize all the equilibrium points in a force field generated by a speckle pattern.
FORMA and BEFORE: Expanding Applications of Optical Tweezers. Laura Pérez Garcia, Martin Selin, Alejandro V. Arzola, Giovanni Volpe, Alessandro Magazzù, Isaac Pérez Castillo.
Submitted to OSA-OMA 2021,  ATh1D.5
Date: 15 April
Time: 15:45 (CEST)

Abstract: 
FORMA (force reconstruction via maximum-likelihood-estimator analysis) addresses the need to measure the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise. Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields. Here, after a brief introduction to FORMA, I will present its use, advantages, and limitations. I will conclude with the most recent work where we exploit Bayesian inference to expand FORMA’s scope of application.

Seminar by R. Ganapathy at Soft Matter Lab, 7 April 2021

On Wednesday, 7 April 2021, Rajesh Ganapathy will give a seminar at the Soft Matter Lab and the Department of Physics, University of Gothenburg. He will speak on how energy can be harvested in microscopic environments making use of active baths.

Tuning the performance of a micron-sized Stirling engine by ‘active’ noise
Rajesh Ganapathy
Time: 07 April, 2021, 11:00
Place: Online via Zoom (link to be shared)

Abstract: Mesoscale heat engines, wherein a single atom or a micron-sized colloidal particle is the working substance, are paradigmatic models to elucidate the conversion of heat into work in a noisy environment. While stochastic thermodynamics provides a precise framework for quantifying the performance of these engines when operating between thermal baths, how energy transduction occurs when the reservoirs themselves are out-of-equilibrium, life for instance for a biological motor carrying cargo inside a cell, remains largely unclear. In the first part of my talk, I will describe the design, construction, and quantification of a colloidal Stirling geat engine operating, in the quasistatic limit, between bacterial baths characterized by different levels of activity. We will show that due to ‘active noise’ the performance of the Stirling engine even surpasses a thermal Stirling engine operating between reservoirs with an infinite temperature difference. In the second part of my talk, we will outline a reservoir engineering approach that allowed us to operate the ‘active’ Stirling engine not only in the quasi-static-limit but also at finite cycle durations. Armed with this capability, we will show that the performance of a micron-sized Stirling engine can be tuned by altering only the nature of the reservoir noise statistics.

Falko Schmidt nominated for a Student Paper Prize at the Biophotonics Congress

Non-spherical nanoparticle held by optical tweezers. The particle is trapped against the cover slide.

Falko Schmidt has been nominated by the Optical Society of America for a Student Paper Prize for Optical Manipulation and its Applications among three other finalists. He will present his work on the Dynamics of an Active Nanoparticle in an Optical Trap at the Optical Manipulation and its Applications meeting as part of the 2021 OSA Biophotonics Congress: Optics in Life Sciences.

Based on the oral presentations of the finalists, the jury will select the winner. Falko Schmidt will present on April 16th at 12:30pm (CEST).

This work is based on the article recently published in Nature Communications.

Non-equilibrium properties of an active nanoparticle in a harmonic potential
Falko Schmidt, Hana Šípová-Jungová, Mikael Käll, Alois Würger & Giovanni Volpe
Nature Communications 12, 1902 (2021)
doi: 10.1038/s41467-021-22187-z
arXiv: 2009.08393

David Bronte Ciriza nominated for a Student Paper Prize at the Biophotonics Congress

Optical forces calculated on a sphere with the geometrical optics (left column) and the machine learning (center column) approaches. The difference between both approaches is shown in the column on the right, illustrating the removal of artefacts with the machine learning method.

David Bronte Ciriza has been nominated by the Optical Society of America for a Student Paper Prize for Optical Manipulation and its Applications among three other finalists. He will present his work on Machine Learning to Enhance the Calculation of Optical Forces in the Geometrical Optics Approximation at the Optical Manipulation and its Applications meeting as part of the 2021 OSA Biophotonics Congress: Optics in Life Sciences.

Based on the oral presentations of the finalists, the jury will select the winner. David Bronte Ciriza will present on April 16th at 5:00pm (CEST).