Thesis – Page 3 – Soft Matter Lab

August Kälvesten, Richard Blücher, Vilhelm Hedquist, Andreas Bauner, Adam Törnkvist, Eric Dat Le defended their Bachelor Thesis on 25 May 2022. Congrats!

August Kälvesten, Richard Blücher, Vilhelm Hedquist, Andreas Bauner, Adam Törnkvist, Eric Dat Le presenting their bachelor thesis. (Photo by A. Callegari.)

August Kälvesten, Richard Blücher, Vilhelm Hedquist, Andreas Bauner, Adam Törnkvist, Eric Dat Le defended their Bachelor Thesis at Chalmers University of Technology on 25 May 2025. Congrats!

Title: Can slower predators catch faster swarming prey?
A study of systems where faster swarming prey interact with slower predators through simulation
Title: Kan långsammare rovdjur fånga snabbare svärmande byten?
En undersökning av system där snabbare svärmande byten interagerar med långsammare rovdjur genom simulation

Abstract: This project examined how predators can catch prey in a predator-prey system where the predators have a lower speed than their swarming prey. The investigated factors were the varied angular velocity of prey and predator, complex environment, and several cooperating predators. This was done through simulations based on the Vicsek model where a base model was modified for each of the investigated factors. When varied angular velocity was investigated it was found that the angular velo- city of the predator didn’t have a large effect on the numbers of prey captured, but what had an effect was the angular velocity of the prey. That could be explained by the predators traveling towards the prey head-on and the prey not being able to turn away fast enough. For complex environments, it was shown that an increased radius and number of obstacles in the environment led to increased numbers of prey caught. This is contradictory to the phenomenon in nature and could be explained by limitations in the model. Finally, when many cooperating predators were introduced, it was found that groups of three or four predators were required for prey to be caught. When many predators were introduced, more such groups could be created and therefore capture more prey. Although only three uncountable factors that govern predator-prey systems have been investigated, there are some indications that slower predators can catch faster swarming prey.

Sammandrag: I detta projekt undersöktes hur rovdjur kan fånga byten i ett rovdjur-bytessystem där rovdjuren har lägre fart än dess svärmande byten. De faktorer som undersökts är varierande vinkelhastighet hos byten och rovdjur, komplexa miljöer, och flera sam- arbetande rovdjur. Detta gjordes genom simuleringar baserat på Vicsek-modellen där en basmodell modifierades för varje faktor som undersöktes. Då varierande vin- kelhastighet undersöktes noterades det att rovdjurets vinkelhastighet inte har någon större inverkan på antalet fångade byten, utan det var snarare bytesdjurens vinkel- hastighet som hade störst inverkan. Det kunde förklaras av att rovdjuren lyckades fånga byten då de färdades rakt mot varandra och bytesdjuret inte kunde svänga av tillräckligt snabbt. För komplexa miljöer visades att en ökad radie och antal hinder i miljön ökade antalet fångade byten. Detta var motsägande observerade fenomen i naturen och kunde förklaras av begränsningar i modellen. Slutligen observerades, när flera samarbetande rovdjur undersöktes, att det krävdes grupper av tre eller fyra rovdjur för att byten skall fångas. Då många rovdjur introducerades kunde flera sådana grupper skapas och därför fånga fler byten. Trots att endast tre av de oräkneliga faktorer som styr rovdjur-bytessystem i verkligheten har undersökts kan vissa indikationer finnas på att långsammare rovdjur kan fånga snabbare svärmande byten.

Nyckelord: Vicsek-modellen, rovdjur-bytessystem, simulering, långsamma rovdjur, svärmande byten

Supervisors: Agnese Callegari and Giovanni Volpe, Department of Physics, University of Gothenburg

Examiner: Lena Falk, Department of Physics, Chalmers University of Technology

Place: FL41
Date: 25 May, 2022
Time: 9:00

Lukas Niese defended his Master thesis on 17 January 2022. Congrats!

Lukas Niese defended his Master thesis in Physics at the Technische Universität Dresden on 17 January 2022. Congrats!

*(Image from Lukas Niese’s Master Thesis)*

Title: Application of Deep Learning for Investigation of Chemotactic Behaviour in Marine Microorganisms

Deep learning has recently become a powerful instrument, enhancing research in many fields and profiting from abundant availability of manifold data sets. In active matter research, medicine and biology there is huge demand of robust and accurate methods to track and analyse micro scale particles and cells in microscopy images. The Pyhton based software Deeptrack 2.0 offers a basic toolkit to build customized deep learning methods for particle localization, classification and tracking. In this project Deeptrack 2.0 was used to track marine microorganisms and investigate their motion in response to chemical stimulants, known as chemotaxis. In addition, the accuracy of particle localization and classification was measured by three different benchmark tests, which imitated shapes and movement of real microorganisms. The results were compared with the performance ofthe algorithmic standard method Trackmate by Fiji ImageJ. Deeptrack 2.0 has shown a significantly better performance for particles with complex shapes and with time varying appearance were to be tacked. However Trackmate is slightly more accurate in locating small particles appearing in Gaussian intensity distribution. In the experimental part two test assays have been developed and proven a facile and robust way to study chemoattraction in the autotrophic green alga Dunaliella tertiolecta. Deeptrack was successfully applied create and analyze the cell trajectories according to velocity and spatial distribution in individuals. Based on the developed combination of experiment and computational analysis, further investigations can be carried out to elucidate the chemical and ecological nature of chemotaxis in Dunaliella tertiolecta.

Adviser: Prof. Giovanni Volpe
Examiner: Prof. Alexander Eychmüller (TU Dresden)
Date: 17 January 2022
Time: 17:00
Place: TU Dresden and Online via Zoom

Thomas Suphona defended his Master thesis on 27 September 2021. Congrats!

Thomas Suphona defended his Master thesis in Physics at the Chalmers University of Technology on 27 september 2021. Congrats!

*(Image from a composition of screenshots during Thomas Suphona’s Master Thesis defense)*

Title: Collective behaviors of autonomous robots in complex environment

Collective behaviours or collective motion is a common phenomena in nature where multiple organisms in a system undergo ordered movements. This can be observed in different scales, from the microscale with bacteria swarming to the macro scale with for example flocks of birds, schools of fish and even human crowds and car traffic.
All these systems are made up by self-propelling agents who are able to take up energy from their environment and converting it to directed motion.
Because of this
property of self-propulsion, their dynamics cannot be explained using conventional methods. Although significant efforts have been made in trying to explain collective behaviours from different perspective, using simulation tools and study systems in different scales as mentioned before, the subject is not as widely studied from the macroscale, especially with artificially made systems. In this thesis, a macroscale system was design with the purpose of providing conditions for collective behaviours to emerge and study how the behaviours changes depending on the surrounding conditions. Battery powered robots were used as self-propelling agents and they were placed in a confined space filled with obstacles. It was shown that when the number of robot and obstacles inside the system is large, the robots movements were significantly restricted. The weight of the obstacles do also affect the average motions of the robots where heavier obstacles hinders the robots by creating blockage leading to the robots having lower average velocity. At certain configuration of the parameters, the robots showed collective behaviours where they for example form channels between the obstacles, making ”roads” for other robots to reuse, or helping each other to move by pushing away chunks of obstacles or pushing onto each other. Even though these robots are simple agents, they have manage to manifest cooperative actions towards other agents.

Supervisor: Giovanni Volpe and Alessandro Magazzú
Examiner: Giovanni Volpe
Opponent: David Fitzek

Date: 27 September, 2021,
Time: 16:00
Place: Online via Zoom
Link: Master thesis presentation Thomas Suphona

Saga Helgadottir defended her PhD Thesis in Physics on June 16, 2021. Congrats!

Saga Helgadottir defended her PhD Thesis in Physics on June 16, 2021. Congrats!

The disputation took place at 9 a.m. digitally via Zoom. A link to the Zoom meeting was published the day before dissertation on the GU website.

Title: Deep Learning Applications – From image analysis to medical diagnosis

Abstract:
Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using explicit rules to perform a desired task as in standard algorithmic approaches, machine-learning algorithms autonomously learn from data to determine the rules for the task at hand. The idea of deep learning has been around since the 1950s but was for a long time limited by available computational power and amount of training data. Once overcome these problems, in recent years, deep learning has made great advances in solving various problems.

In this thesis, I show how deep learning can be applied in image analysis and medical diagnosis, while outperforming standard algorithmic methods and simpler machine-learning methods. I begin with showing that a convolutional neural network trained with simulated particle images is able to track experimental single particles, even in poor illumination conditions. I then show how this inspired the development of an all-in-one software to design, train and validate deep-learning solutions for digital microscopy, from particle tracking and characterization in 2D and 3D to the segmentation, characterization and counting of biological cells and image transformation. I show that this software package can be further used to develop a generative adversarial neural network to virtually stain brightfield images of cells, replacing the traditional chemical staining for a downstream analysis of biological features. I then move on from applications in microscopy and image analysis to show the potential of deep learning in medical diagnosis. I show that dense neural networks perform better than simpler machine-learning algorithm and the clinical standard in the diagnosis of a genetic disease and in the prediction of short- and long-term morbidity in patients with congenital-heart-disease. At last, I have shown that a neural network- powered strategy for testing and isolating individuals adapts to the parameters of a disease outbreak achieves an epidemic containment.

The interdisciplinary nature of the work in this thesis has allowed the application of new technologies developed in the field of physics to solve problems in the fields of biology and biomedicine, as well as overcoming barriers for the continued revolutionization of deep learning in microscopy.

Thesis: http://hdl.handle.net/2077/67506

Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Carolina Wählby
Committee: Marj Tonini, Maria Garcia-Parajo, Alexander Rohrbach

Screenshots from Saga Helgadottir’s PhD Thesis defense.

PhD Thesis presentation: Saga Helgadottir, Giovanni Volpe (Supervisor), Raimund Feifel (GU Physics), Carolina Wählby (Opponent), Marj Tonini (Committee member), Maria Garcia-Parajo (Committee member), Måns Henningson (GU Physics Department Chair), Alexander Rohrbach (Committee member).

PhD Thesis presentation content slide (1).

PhD Thesis presentation content slide (2).

PhD Thesis presentation conclusion slide.

Olle Fager defended his Master thesis on 15 June 2021. Congrats!

Olle Fager defended his Master thesis in MPCAS at the Chalmers University of Technology on 15 June 2021. Congrats!

Title: Real-Time Multi-Object Tracking and Segmentation with Generated Data using 3D-modelling

Multi-Object Tracking and Segmentation (MOTS) is an important branch of computer vision that has applications in many different areas. In recent developments these methods have been able to reach favorable speed-accuracy trade-offs, making them interesting for real-time applications. In this work different deep learning based MOTS methods have been investigated with the purpose of extending the DeepTrack framework with real-time MOTS capabilities. Deep learning methods rely heavily on the data on which they are trained. The collection and annotation of the data can however be very time-consuming. Therefor, a pipeline is developed and investigated that automatically produces synthetic data by utilizing 3D-modelling. The most accurate tracker achieves a MOTSA score of 94 and the tracker with the best speed-accuracy trade-off achieves a MOTSA score of 88. It is also observed that satisfactory results can be achieved in most situations with a quite general data generation pipeline, indicating that the developed pipeline could be used in different scenarios.

Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Arianit Zeqiri and Morad Mahmoudyan

Place: Online via Zoom
Time: 15 June, 2021, 16:00

Aykut Argun defended his PhD Thesis in Physics on 14 June 2021. Congrats!

Aykut Argun defended his Ph.D. thesis on June 14, 2021, at 2 pm CEST. Congrats!

The details of the presentation can be found below. The link to the webinar is announced on the faculty website.

Title: Thermodynamics of microscopic environments: From anomalous diffusion to heat engines.

Abstract:
Unlike their macroscopic counterparts, microscopic systems do not evolve deterministically due to the thermal noise becoming prominent. Such systems are subject to fluctuations that can only be studied within the framework of stochastic thermodynamics. Within the last few decades, the development of stochastic thermodynamics has lead to microscopic heat engines, nonequilibrium relations and the study of anomalous diffusion and active Brownian motion. In this thesis, I experimentally show that the non-Boltzmann statistics emerge in systems that are coupled to an active bath. These non-Boltzmann statistics that result from correlated active noise also disturb the nonequilibrium relations. Nevertheless, I show that these relations can be recovered using an effective potential approach. Next, I demonstrate an experimental realization of a microscopic heat engine. This engine is referred to as the Brownian gyrator, which is coupled to two different heat baths along perpendicular directions. I show that when confined into an elliptical trap that is not aligned with the temperature anisotropy, the Brownian particle is subject to a torque due to the symmetry breaking. This torque creates an autonomous engine whose direction and amplitude can be controlled by tuning the alignment of the elliptical trap. Then, I show that the force fields acting on Brownian particles can be calibrated using a data-driven method that outperforms the existing calibration methods. More importantly, I show that this method, named DeepCalib, can calibrate non-conservative and time-varying force fields that no standard calibration methods exist. Finally, I show that a similar machine-learning-based approach can be used to characterize anomalous diffusion from single trajectories. This method, named RANDI, is very versatile and performs very well in various tasks including classification, inference and segmentation of anomalous diffusion. The work presented in this thesis presents novel experiments that advance microscopic thermodynamics as well as newly developed methods that open up new possibilities in analyzing stochastic trajectories. These findings increased the scientific knowledge at the nexus between microscopic thermodynamics, anomalous diffusion, active matter and machine learning.

Supervisor: Giovanni Volpe
Co-supervisors: Joakim Stenhammar, Mattias Goksör
Examiner: Bernhard Mehlig
Opponent: Juan M. R. Parrondo
Committee: Monika Ritsch-Marte, Sabine H. L. Klapp, Édgar Roldán

Screenshots from Aykut Argun’s PhD Thesis defense.

PhD Thesis Committee, Supervisor, Co-Supervisor, Opponent, and GU Physics Department Chair.

PhD Thesis Committee, Supervisor, Opponent, and GU Physics Department Chair.

Agaton Fransson defended his Master thesis on 4 June 2021. Congrats!

Agaton Fransson defended his Master thesis in MPCAS at the Chalmers University of Technology on 4 June 2021. Congrats!

Big plankton tracked by network-based software in a sample of big (Strombidium arenicola) and small plankton (Rhodomonas baltica). *(Image by Agaton Fransson)*

Title: Tracking plankton using neural networks trained on simulated images

Softwares to track particles often use algorithmic approaches to detect particles and to create tracks using the found positions, requiring human fine-tuning of parameters to achieve sought-for results. This can be time consuming and difficult, while also creating opportunities for human error and bias. With the developments of computational power and machine learning techniques such as deep learning, data driven approaches have made their way into many fields of science. Barriers preventing advances of such methods are the lack of available training data within a field and the level of proficiency required to create custom machine learning solutions. DeepTrack 2.0 is a software providing us with means to simulate digital microscopy images, build and train neural networks such as U-nets. In this paper DeepTrack 2.0 is utilized and built on to fit the needs of marine biologists when tracking plankton. Here I show that DeepTrack 2.0 provides us with the tools necessary to detect and track different types of plankton filmed in a variety of conditions with performance on par with and with the potential to outperform conventional tracking softwares. I also show that for plankton in a messy environment moving uniformly a network trained to detect motion rather than a shape proves more successful. These results demonstrate the versatility of deep learning methods and the potential of training networks on simulations for applications on real data, as is the case for marine biologists studying plankton. They also show the impact the structure of the training data has on the nature of the network.

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

Place: Online via Zoom
Time: 4 June, 2021, 15:00

Kevin Andersson and Eric Lindgren defended their Master thesis on 2 June 2021. Congrats!

Kevin Andersson and Eric Lindgren defended their Master thesis on 2 June 2021. Congrats!

Title: Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction
Subtitle: Identifying important functional brain networks using explainability in Graph Convolutional Networks

Insights into how biological sex and healthy ageing affects the human brain are important for an increased understanding of the brain. Healthy ageing insights are also useful for clinical applications, for instance in identifying unhealthy ageing due to neurodegenerative disease. To this end, several studies in the last few years have used machine learning methods on neuroscientific data to predict subject sex and brain age. One particularly interesting approach has been to represent functionally connected networks in the brain as graphs, and apply Graph Convolutional Networks (GCNs). To investigate which functional brain networks are connected with sex and age, we develop and analyse GCN-based models that predicts sex and age from resting-state fMRI data. The analysis of the models is done using saliency mapping techniques which gives insight into what functional brain networks in the data are relevant for the predictions. With this approach, we obtain a sex prediction accuracy of up to 79% and an age prediction MAE of 5.9 years. Furthermore, we find indications that the Sensory Motor Network and the cerebellum are among the more important functional brain networks for predicting sex and brain age.

Master programme: Physics
Supervisor: Alice Neimante Diemante (Syntronic AB)
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Rasmus Svensson

Place: Online via Zoom
Time: 2 June, 2021, 16:00

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

Sammandrag:
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.

Abstract:
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.

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