Presentation by E. Erdem, 4 October 2023

Schematic of a red blood cell in a focused optical beam. (Image by the E. Erdem.)
Optical trapping of red blood cells and different geometrical shapes
Emir Erdem

Red Blood Cells (RBC), also known as erythrocyts, are essential cells that are present in the blood of every vertebrate. Because of their hemoglobin protein content, they carry oxygen to the cells and perform a vital function. Due to their complex shapes, behavior of cells like RBCs under optical forces are not fully been discovered. In this study, the behavior of RBCs as well as other shapes under optical trap are simulated using OTGO which is a numeric toolbox utilizing geometrical optics approximation for optical calculations. As a result of the simulations, it is observed that the RBC aligns itself in a vertical configuration, parallel to the incident beam propagating towards the cell from below. Conducted static analysis showed that it is possible to stably trap a RBC in all three dimensions. The center of the trap is near the edge of the cell, where the thickness is larger. After the analysis on RBC, how well different geometrical shapes can optically be trapped are investigated by integrating different shapes modeled by spherical harmonics to OTGO. A similar static analysis is conducted on a dumbbell shape and its trapping effectiveness is compared with an ellipsoid. A dumbbell shape can effectively be trapped in the horizontal plane similar to an ellipsoid, but in the light propagation direction, it is more challenging to trap the shape and it requires modifications on optical properties of the setup. The aim of this study after this point is to optimize the optical force calculations by training a neural network model and to apply flow conditions to cells.

Giovanni Volpe awarded the Faculty of Science’s 2023 Research Award

(Image adapted from here.)
Giovanni Volpe received the Faculty of Science’s 2023 Research Award for using methods from physics to look into complex and biological systems.

The Research Award of the Faculty of Science of the University of Gothenburg recognizes development of a research specialization that significantly contributes to novelty in the faculty’s research. The award recipient receives a diploma and a research grant of SEK 250,000. This year, the award ceremony will be held on 19 October.

A short interview with Giovanni Volpe regarding this achievement can be found at the link: Giovanni Volpe awarded the Faculty of Science’s 2023 Research Award.

Presentation by H. Bachimanchi at International Forum for Computer vision in Ecology and Evolution, Lund University, 21 September 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Bringing microplankton to focus: Holography and deep learning
Harshith Bachimanchi
21 September 2023, 11:15 AM 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.

Seminar by M. Karg on 20 September 2023

Drying of a microgel monolyer. (Image by M. Karg.)
Microgel monolayers at liquid interfaces: In situ analysis and role of uniaxial compression
Matthias Karg

20 September 2023, 12:30, Nexus

Microgels are soft polymeric objects with an internal gel-like structure and overall dimensions in the colloidal regime [1]. It is known that microgels strongly adsorb to liquid/liquid and liquid/air interfaces. Many studies in the last two decades attempted to understand the phase behavior of soft, deformable microgels at such liquid interfaces. Typically, the microstructures in dependence on applied surface pressure are studied ex situ using transfer of microgel monolayers from the liquid to a solid interface followed by investigation with different types of microscopies. Interestingly, in situ studies at the liquid interface are scare to nonexistent.
We tackled two challenges in this respect: 1) We managed to synthesize core-shell microgels that are large enough to be studied by optical microscopy or small-angle scattering using light [2]. 2) We build a setup that combines a Langmuir trough with small-angle light scattering (LTSALS) that allows for the large area study of monolayers during compression with excellent resolution in time [3]. In this work we present first results of the in situ analysis of microgel monolayers at air/water interfaces. Instead of the commonly reported solid-solid isostructural phase transition [4,5], we find a continuous compression of the monolayer with continuously decreasing interparticle distances [3]. Furthermore, drying of a thin liquid film with the monolayer at the liquid/air interface on hydrophilic and hydrophobic substrates shines light on the complex interplay between softness, adhesion and capillary interactions. We then studied the role of uniaxial compression/expansion by using our LT-SALS setup. Upon compression and/or expansion the monolayer remains somewhat anisotropic and a fast and a slow relaxation process is observed during an equilibration phase, i.e. when compression or expansion is stopped. Possible explanations for this behavior will be discussed.

[1] M. Karg, et al., Langmuir, 2019, 35, 6231-6255.
[2] K. Kuk, L. Gregel, V. Abgarjan, C. Croonenbrock, S. Hänsch, M. Karg, Gels 2022, 8, 516.
[3] K. Kuk, V. Abgarjan, L. Gregel, Y. Zhou, V. Carrasco-Fadanelli, I. Buttinoni, M. Karg, Soft
Matter, 2023, 19, 175-188.
[4] M.Rey, et al., Soft Matter, 2016, 12, 3545-3557.
[5] A. Rauh, et al., Soft Matter, 2017, 13, 158-169

Deep-learning-powered data analysis in plankton ecology on ArXiv

Segmentation of two plankton species using deep learning (N. scintillans in blue, D. tertiolecta in green). (Image by H. Bachimanchi.)
Deep-learning-powered data analysis in plankton ecology
Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert, Pierre De Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander, Giovanni Volpe
arXiv: 2309.08500

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Alfred Bergsten will defend his Master Thesis on 18 September 2023

Trajectory of a hexagonal cluster formed by a transparent particle (blu circle) and six light-absorbing particles (red circles) in a traveling sinusoidal optical pattern, in the absence of thermal noise. The direction of the motion of the optical pattern is given by the arrow. The trajectory’s duration is 30 s. (Image by A. Bergsten.)
Alfred Bergsten will defend his Master Thesis on 18 September 2023 at 17:00.

Title: Controlling Active Clusters Using Wave-Shaped Light Patterns

Colloidal systems appear in various contexts. In some of these systems, thermophoretic forces can arise around otherwise passive particles when they are illuminated, leading to the emergence of complex behaviours. These types of systems has been extensively studied under constant, uniform light where the emergent behaviours are simply activated and deactivated. The aim of this project is to show that the emergent behaviour can not only be activated and deactivated, but also controlled by employing more complex light patterns.
The model used in this project includes Brownian motion and thermophoretic forces, with collisions between particles being resolved by a volume exclusion method. The thermophoretic forces are activated by employing travelling wave light patterns to affect the behaviours of different clusters formed as a result of these forces. Two different patterns are then superimposed to show that more complex light patterns can induce more complex behaviours.
This study is mostly qualitative in nature and only conducted in simulations. While the parameter space has only been roughly explored and the study needs to be validated through physical experiments, the results of the project indicate that a more comprehensive exploration of the parameter space for a broader range of clusters can be of interest.

Supervisor: Agnese Callegari
Examiner: Giovanni Volpe
Opponent: Simon Carlson

Place: Nexus
Time: 18 September, 2023, 17:00

Talk by K. Porter (IOP Publishing), 6 September 2023

(Photo by G. Volpe.)
How to get published: a talk from IOP Publishing
Kate Porter
IOP Publishing

Do you want your article to stand out from the crowd, improving your chances of publication in this highly competitive industry? If so, you won’t want to miss this talk from Kate Porter, Senior Publisher from IOP Publishing! During this talk, Kate will provide you with a toolkit to help you navigate the world of academic publishing and share some top tips to help you get published.

Topics covered in this talk include:

  • Choosing the right journal for your research
  • Open access and transformative agreements
  • Publication ethics
  • Top tips for writing your article so it captures the interest of editors/reviewers
  • Peer review and responding to reviewers
  • Post-acceptance activities to promote your article

Date: 6 Sep 2023
Time: 12:30 PM
Location: PJ

Kate Porter in PJ salen. (Photo by G. Volpe.)
PhD students at the faculty of science attending the seminar. (Photo by G. Volpe.)

Gideon Jägenstedt joins as PhD student the Soft Matter Lab

(Photo by A. Argun.)
Gideon Jägenstedt started his PhD at the Physics Department of the University of Gothenburg on the 1st of September 2023.

Gideon has a master degree in Complex Adaptive Systems from Chalmers University of Technology.

In his PhD, Gideon will focus his research on deep learning.

Mirja Granfors joins as PhD student the Soft Matter Lab

(Photo by A. Argun.)
Mirja Granfors starts her PhD at the Physics Department at the University of Gothenburg on 1st September 2023.

Mirja has a Master degree in Physics from the University of Gothenburg.

In her PhD, Mirja will focus on graph neural networks and deep learning.