Giovanni Volpe
DINAMO 2023, Svolvaer, Lofoten Islands, Norway
Date: 15 June 2023
Time: 08:30
News
Poster by G. Wang at DINAMO 2023, Svolvær, 13 June 2023
Gan Wang, Marcel Rey, Mahdi Shanei, Kunli Xiong, Einstom Engay, Mikael Käll, and Giovanni Volpe
Date: 13 June 2023
Time: 21:00 (CEST）
On-chip micromotors hold significant application potential in various fields, including cells, microfluidic manipulation, and the micro integration of machines. .The driving mechanism plays a crucial role in the design of micromotors. Currently, various methods such as static electricity, light, magnetism, chemical energy, and mechanical conduction are utilized for this purpose. Optics, in particular, offers distinct advantages including precise control, addressability, non-contact operation, and compatibility with diverse liquid environments. However, existing optically actuated on-chip motors necessitate high energy input, resulting in phototoxicity concerns and hindrances to large-scale manipulation. Furthermore, achieving precise control over speed and direction remains challenging, along with difficulties in establishing coupling among multiple devices.
Christian Rutgersson defended his Master Thesis on 9 June 2023. Congrats!
Christian Rutgersson defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 9 June 2023 at 17:00. Congrats!
Title: Characterizing Active Matter Particle Systems with Graph Neural Networks
Abstract:
Biological systems often have self-organizing properties. On the microscopic scale, the self-organization may be driven by constituents that generate their own motility by expending energy. Systems made up of such constituents, that self-propel, are termed active matter. Via this definition, it is clear that it is of importance to understand the rules that govern the microscopic constituents of an active matter in order to understand the active matter itself. Even though active matter is a topic of interest today, finding good methods of qualitatively and quantitatively characterizing the constituents of active matter remains an issue. Coincidentally, different types of artificial neural networks (ANN) have, in recent years, been used increasingly in research settings with great success. One such network is called the graph neural network (GNN). As the name suggests, this network is specifically designed to work with graphs as input data. Graphs can act as a useful representation of a system of particles, including active matter systems. Therefore, this project aims to characterize the forces that underpin an active matter system consisting of interacting particles that also have an active component, using a special type of GNN called message passing network (MPN). This was done by simulating the active matter using a Python code written from scratch, and training the MPN with standard machine learning algorithms. In the end, the simulations were found to give rise to characteristic active matter phenomena, and the MPN was able to correctly predict the force dynamics of a particle in the given active matter system.
Supervisors: Miguel Ruiz Garcia, Carlo Manzo, Jesus Pineda Castro, Giovanni Volpe
Examiner: Giovanni Volpe
Opponent: Ludvig Lindahl
Place: Nexus
Time: 9 June, 2023, 12:00
Gideon Jägenstedt defended his Master Thesis on 8 June 2023. Congrats!
Title: End-to-End Object Tracking on Simulated Microscopy Video
Abstract:
Object tracking in microscopy time-lapse videos is currently mostly done in two steps often using two neural network models, first the images are segmented in order to detect each object within each time frame and extract their centroids using one neural network model. A selected set of properties on the centroids are then used as an input to a second neural network that creates the temporal trajectories by linking the centroids over a sequence of frames.
This work proposes a novel method to combine the object detection step and the
linking step which should, in theory, create better linking in time since the combined model has access to not only a set of properties but the complete image of the centroids. Two different architectures of a combined model were tested, one supervised model based on graph neural networks (GNN) and one unsupervised model based on a variational autoencoder (VAE).
The supervised GNN-based model did not succeed in predicting the position of the centroids, but it showed promise in linking the centroids between frames. Therefore, the VAE-based model was developed that uses the same approach for linking. The VAE-based model resulted in a mean absolute error of under 0.002 on its detection placement, a detection miss-rate of 2.69 %, and an F1-score of 81.2 % when linking trajectories on simulated data.
Supervisor: Jesus Pineda Castro
Examiner: Giovanni Volpe
Opponent: Mirja Granfors
Place: PJ
Time: 8 June, 2023, 11:00
Mirja Granfors defended her Master thesis on June 8, 2023. Congrats!
Title: Enhancing Graph Analysis and Compression with Multihead Attention and Graph-Pooling Autoencoders
Abstract:
Graphs are used to model complex relationships in various domains. Analyzing and classifying graphs efficiently poses significant challenges due to their inherent structural complexity. This thesis presents two distinct projects aimed at enhancing graph analysis and compression through novel and innovative techniques. In the first project, a multihead attention module for node features is developed, enabling effective prediction of graph edges for connection in time. By applying attention mechanisms, the module selectively focuses on relevant features, facilitating accurate edge predictions. This approach expands the potential applications of graph analysis by improving the understanding of graph connectivity and identifying critical relationships between nodes. The second project introduces a novel graph autoencoder with multiple steps of size reduction by graph-pooling. Unlike traditional graph autoencoders, which commonly employ graph convolutional networks, this approach utilizes several poolings to capture diverse structural information and compress the graph representation. The pooling-based autoencoder not only achieves efficient graph compression but also captures the structural information of the graph. This enables the classification of graphs based on their structure, providing a valuable tool for tasks such as graph categorization.
Supervisor: Jesús Pineda
Examiner: Giovanni Volpe
Opponent: Gideon Jägenstedt
Place: PJ-Salen
Time: 8 June, 2023, 10:00
John Klint and Niphredil Klint defended their Master Thesis on 7 June 2023. Congrats!
John Klint and Niphredil Klint defended their Master thesis in Physics at the University of Gothenburg on 7 June 2023 at 17:00. Congrats!
Abstract:
Active matter systems can be found on many different length- and time-scales in nature. Tiny molecular machines, colonies of bacteria and swarming insects are all examples of such systems. What they all have in common is that they are composed of agents that convert energy into different types of directed motion. This activity occurs only when the agents are in a non-equilibrium state. Often, the interactions give rise to emergent behaviours otherwise not observed for single individuals. A key aspect of active matter systems is that without an energy source, the agents do not exhibit any directed motion and therefore no emergence. The energy source may consist of, for example, light, heat, chemical reactions or vibrations.
Research into active matter often involves laboratory experiments and these can be both expensive and time-consuming to set up. In this project, we explore a simple yet powerful numerical method designed to be efficient but still capable of capturing essential phenomena of light-activated systems. We consider two distinct types of colloidal particles, one that absorbs light and one that does not absorb light. When light is absorbed by one of the particle species, a temperature gradient is generated. Both types of particles are attracted to higher temperatures, and this phoretic attraction is the only interaction at a distance considered between the particles. Since the only particles that generate a temperature gradient are the ones that absorb light, there is an effective non-reciprocal phoretic interaction, which is directed from centre to centre. To avoid unphysical overlaps in the simulation, we implemented a volume exclusion scheme to account for the finite size and hard-core nature of the particles.
Through simulations, we examined and catalogued emergent properties for clusters of particles and statistically determined their speed and rotational frequency. We also investigated cluster lifetimes and categorised different formations of active colloidal molecules. Furthermore, we implemented a number of different ways to simulate the illumination of the agents, from homogeneous light to square and Gaussian light pulses. We successfully induced several emergent properties, such as cluster disintegration immediately followed by regeneration (in a cellular automata-like fashion), as well as speed and rotational frequency modulation and orientation of clusters in the direction of the wavefront.
The results obtained from our simulations are in agreement with previous experimental research on similar non-reciprocal systems governed by phoretic interactions. Our model and its implementation is capable of capturing a wide range of emergent behaviours. We have confirmed that the model can be used to explore how various light environments influence the behaviour of light-activated agents. The minimalistic approach of our work can be seen as a vantage point for further numerical studies of active matter systems.
Examiner: Giovanni Volpe
Supervisor: Agnese Callegari
Opponent: Mathias Samuelsson
Place: von Bahr
Time: 7 June, 2023, 17:00
Presentation by H. Bachimanchi at AI for Scientific Data Analysis, Gothenburg, 1 June 2023
Harshith Bachimanchi
1 June 2023, 15:00 CEST
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.
Presentation by J. Pineda at AI for Scientific Data Analysis, Gothenburg, 1 Jun 2023
Jesús Pineda
Characterizing dynamic processes in living systems provides essential information for advancing our understanding of life processes in health and diseases and for developing new technologies and treatments. In the past two decades, optical microscopy has undergone significant developments, enabling us to study the motion of cells, organelles, and individual molecules with unprecedented detail at various scales in space and time. However, analyzing the dynamic processes that occur in complex and crowded environments remains a challenge. This work introduces MAGIK, a deep-learning framework for the analysis of biological system dynamics from time-lapse microscopy. MAGIK models the movement and interactions of particles through a directed graph where nodes represent detections and edges connect spatiotemporally close nodes. The framework utilizes an attention-based graph neural network (GNN) to process the graph and modulate the strength of associations between its elements, enabling MAGIK to derive insights into the dynamics of the systems. MAGIK provides a key enabling technology to estimate any dynamic aspect of the particles, from reconstructing their trajectories to inferring local and global dynamics. We demonstrate the flexibility and reliability of the framework by applying it to real and simulated data corresponding to a broad range of biological experiments.
Date: 1 June 2023
Time: 10:15
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference
Presentation by A. Callegari at AI for Scientific Data Analysis, Gothenburg, 31 May 2023
Agnese Callegari
Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.
Date: 31 May 2023
Time: 10:45
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference
Presentation by C. B. Adiels at AI for Scientific Data Analysis, Gothenburg, 31 May 2023
Caroline B. Adiels
Chemical live/dead assay has a long history of providing information about the viability of cells cultured in vitro. The standard methods rely on imaging chemically-stained cells using fluorescence microscopy and further analysis of the obtained images to retrieve the proportion of living cells in the sample. However, such a technique is not only time-consuming but also invasive. Due to the toxicity of chemical dyes, once a sample is stained, it is discarded, meaning that longitudinal studies are impossible using this approach. Further, information about when cells start programmed cell death (apoptosis) is more relevant for dynamic studies. Here, we present an alternative method where cell images from phase-contrast time-lapse microscopy are virtually-stained using deep learning. In this study, human endothelial cells are stained live or apoptotic and subsequently counted using the self-supervised single-shot deep-learning technique (LodeSTAR). Our approach is less labour-intensive than traditional chemical staining procedures and provides dynamic live/apoptotic cell ratios from a continuous cell population with minimal impact. Further, it can be used to extract data from dense cell samples, where manual counting is unfeasible.
Date: 31 May 2023
Time: 10:30
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference