Pernilla Huynh, Hannes Johansson, Olof Lind Stefansson, Oskar More Arvidsson, William Olsson, Filip Sterner defended their Bachelor Thesis at Chalmers University of Technology on 27 May 2021. Congrats!

Pernilla Huynh, Hannes Johansson, Olof Lind Stefansson, Oskar More Arvidsson, William Olsson, Filip Sterner defended their Bachelor Thesis at Chalmers University of Technology on 27 May 2021. Congrats!

Title: Kollektiva beteenden hos aktiva agenter i komplexa system
Topologiska interaktioner och skillnader med tidsfördröjning

Studierna av aktiva agenters beteende i komplexa system har rönt stort intresse den senaste tiden. Dels då det är en passande modell för att beskriva många biologiska system, men också för deras för potentiella tillämpningar. Syftet med studien är att undersöka skillnader mellan beteendet för agenter som interagerar genom metriska interaktioner (det vill säga beroende av det metriska avståndet mellan agenter) och agenter som interagerar genom topologiska interaktioner (beroende på ett bestämt antal närliggande agenter). Framförallt studeras hur de båda modellerna förändras då en tidsfördröjning mellan agenternas uppfattning och reaktion introduceras. Undersökningen utförs genom analys av tre olika komplexa system: (1) agenter som rör sig bland periodiska hinder; (2) agenter som följer en ledare genom en labyrint; och (3) aktiva agenters interaktioner med passiva agenter. Utifrån de resultat som erhålls kan det framgångsrikt observeras skillnader i interaktionerna hos den topologiska modellen gentemot den metriska modellen: de aktiva agenterna kan i den topologiska modellen interagera mer med varandra trots periodiska hinder, en större andel agenter tar sig genom labyrinten och klusterbildningen är oftast lägre i systemet med ett lågt antal passiva agenter. Resultaten tyder också på att den topologiska interaktionen i många fall är mindre känslig för tidsfördröjning.

Studies of the behavior of active agents in complex systems have received a lot of interest recently. This interest derives from the fact that active agents provide an ideal model to describe many biological systems and also because of their potential applications. The purpose of this study is to explore which differences there are between the behaviour of agents that interact through metric interactions (i.e. depending on the metric distance between agents) and those of agents that interact through topological interactions (i.e. depending on a certain number of surrounding agents). This report also discusses how the interaction models for active agents change when a time delay between sensing and acting is introduced. These investigations were made by analyzing three different complex environments: (1) agents moving in the presence of periodic obstacles; (2) agents following a leader in a maze; and (3) active agents interacting with passive agents. Based on the results obtained from this study, we could successfully observe differences in the topological interaction compared to the metric model: the active agents interacted more frequently with each other when using the topological model despite periodic obstacles, a larger proportion of agents managed to pass through the maze and the cluster formation was usually smaller in the system with a low number of passive agents. The result also shows that the topological interaction is less sensitive to time delay.

Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, Chalmers University of Technology
Time: 27 May, 2021

Gustaf Sjösten defended his Master thesis on 17 May 2021. Congrats!

Gustaf Sjösten defended his Master thesis in MPCAS at the Chalmers University of Technology on 17 May 2021. Congrats!

Artistic rendering of a light source illumination a single biomolecule and scattered photons. (Image by Gustaf Sjösten, inspired by an image created by Barbora Spackova)
Title: Deep Learning for Nanofluidic Scattering Microscopy

A novel technique for label-free, real-time characterization of single biomolecules called Nanofluidic Scatter Microscopy (NSM) has recently been developed by the Langhammer research group at Chalmers. We have created a machine learning (ML) framework consisting of deep convolutional neural networks such as U-nets, ResNets and YOLO in order to characterize single biomolecules through kymographs collected through NSM, as an alternative approach to a standard data analysis method (SA). As a laser irradiates visible light onto single biomolecules freely diffusing in solution inside nanofluidic channels, the biomolecule and the nanochannel scatter light coherently into the collection optics, such that the nanochannels improve the optical contrast of the imaged biomolecules by several orders of magnitude. A video of the total scattering intensity is then recorded with a high frame rate camera (capturing 200 fps) in order to capture the movement of the molecules as well as the optical contrast of the biomolecules with respect to the nanochannel. From the movement of one single biomolecule, it is possible to predict its diffusion constant, which can then be used to infer the hydrodynamic radius of the biomolecule. Additionally, the predicted optical contrast of one single biomolecule can in turn be used to infer its molecular weight. From the combination of hydrodynamic radius and molecular weight, information about the conformal state of single biomolecules can be inferred. In this thesis, we show that the ML approach yields results comparable to the SA which was developed independently of the ML technique for biomolecules in the weight span 66-669 kDa, and we also show that the ML technique is superior to the SA in other regards, such as computational speed and potential to characterize smaller molecules. The results of the data analysis performed with the ML framework will also make an appearance in the first paper on the NSM technique which has been submitted for publication and is currently under review.

​Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe, Daniel Midtvedt
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Anton Jansson

Place: Online via Zoom
Time: 17 May, 2021, 16:00

Seminar by G. Volpe at Vi2, University of Uppsala, 17 May 2021

Deep learning for particle tracking. (Image by Aykut Argun.)
Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
Vi2 Seminar (Visual Information and Interaction)
University of Uppsala
17 May 2021, 14:15 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.

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, ) 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)

Objective comparison of methods to decode anomalous diffusion on ArXiv

Cells migrating in a 3-dimensional matrix. The color code of the trajectories represents time. (Picture from Fig.1b of the article).
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
arXiv: 2105.06766

Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in trans- port dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual tra- jectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Re- cently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse con- ditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers.

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology, 2 035007 (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717

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.

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
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.