Laura Pérez presented the work “FORMA and BEFORE: expanding applications of optical tweezers” at the ELS conference (online) on the 13th of July.
The main objective of the Electromagnetic and Light Scattering Conference (ELS) is to bring together scientists and engineers studying various aspects of light scattering and to provide a relaxed academic atmosphere for in-depth discussions of theoretical advances, measurements, and applications.
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.
ELS 2021 Date: 13 July 2021 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.
Deep learning for particle tracking. (Image by Aykut Argun)Deep learning for microscopy, optical trapping, and active matter
Giovanni Volpe
Colloquium
(online at) TU-Darmstadt, Germany
18 June 2021, 14:00 CEST
After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter.
In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles.
Finally, I will provide an outlook for the application of deep learning in photonics and active matter.
Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)Improving epidemic testing and containment strategies using machine learning. Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe. Submitted to SDS2020 Date: 11 June Time: 16:15 (CEST)
Abstract:
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 prediction, 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.
Deep learning for particle tracking. (Image by Aykut Argun.)Quantitative Digital Microscopy with Deep Learning Giovanni Volpe
Seminar Vi2 Seminar (Visual Information and Interaction)
University of Uppsala
17 May 2021, 14:15 CEST
Online
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.
Bio: Giovanni Volpe is Professor at the Physics Department at the University of Gothenburg (Gothenburg, Sweden), where he has been leading the Soft Matter Lab since 2016. He has established a strong research group of 18 people (3 postdocs, 12 PhD students, 3 Master students, http://www.softmatterlab.org ) with an externally-funded, ambitious and interdisciplinary research program that combines soft condensed matter, optical manipulation, nanotechnology, and machine learning. He has attracted external funding exceeding 6M €, including several national and European grants such as the ERC-StG ComplexSwimmers (2016-2021) and the ERC-CoG MAPEI (2021-2026). He is a co-funder of the startup companies Lucero Bio and IFLAI.
Link: Vi2 Seminars (zoom link included in the webpage)
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.
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.
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.
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.
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.
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.
