Recent eLife article on plankton tracking gets featured on Swedish national radio

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
The article Microplankton life histories revealed by holographic microscopy and deep learning gets featured on Vetenskapradion Nyheter (Science radio) operated by Sveriges Radio (Swedish national radio) on November 7, 2022.

The short audio feature (Hologram hjälper forskare att förstå plankton) which highlights the important results of the paper (in Swedish) is now available for public listening.

Vetenskapradion Nyheter airs daily news, reports and in-depth discussions about latest research.

Press release on Microplankton life histories revealed by holographic microscopy and deep learning

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
The article Microplankton life histories revealed by holographic microscopy and deep learning has been featured in the news of University of Gothenburg (in English & Swedish) and in the press release of eLife (in English).

The study, now published in eLife, and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, demonstrates how the combination of holographic microscopy and deep learning provides a strong complimentary tool in marine microbial ecology. The research allows quantitative assessments of microplankton feeding behaviours, and biomass increase throughout the cell cycle from generation to generation.

The study is featured also in eLife digest.

Here are the links to the press releases:
Researchers combine microscopy with AI to characterise marine microbial food web (eLife, English)
Holographic microscopy provides insights into the life of microplankton (GU, English)
Hologram ger insyn i planktonens liv (GU, Swedish)
The secret lives of microbes (eLife digest)

Microplankton life histories revealed by holographic microscopy and deep learning published in eLife

Tracking of microplankton by holographic optical microscopy and deep learning. (Image by H. Bachimanchi.)
Microplankton life histories revealed by holographic microscopy and deep learning
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe
eLife 11, e79760 (2022)
arXiv: 2202.09046
doi: 10.7554/eLife.79760

The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards 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 oceanic 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. This method is particularly useful to explore the flux of carbon through micro-zooplankton, the most important and least known group of primary consumers in the global oceans. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.

Presentation by H. Bachimanchi at ISMC 2022, Poznan, 19 September 2022

Plankton tracking with holographic microscope and deep learning. (Image by H. Bachimanchi.)
Quantitative microplankton tracking with holographic microscopy and deep learning
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe
Presentation at ISMC 2022
Poznan, Poland
19 September 2022, 12:40 CEST

A droplet of sea water contains an entire ecosytem. There are microscopic plants, the phytoplanktons, which produce oxygen by absorbing carbon dioxide from the atmsphere by the process of photosynthesis. There are microscopic animals, the microzooplankton, which feed on the phytoplankton. In oceanic ecology, phytoplanktons consume around 65 peta grams of carbon annually, producing approximately 50% of oxygen on the Earth. Microzooplankton take on the role of herbivores, and consume about two thirds (40 Pg carbon) of this primary production. Despite their central importance, our understanding of the phytoplankton and microzooplankton in shaping oceanic communities is still much less developed at a single plankton level.
Here, we demonstrate that by combining holographic microscopy with deep learning, we can follow microplanktons through generations, by continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data, and allow measurements over extended periods of time. 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. We exemplify this by detailed descriptions of microzooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.

Soft Matter Lab members present at ISMC 2022, Poznan, 19-23 September 2022

The Soft Matter Lab participates to the ISMC 2022 in Poznan, Poland, 19-23 September 2022, with the presentations listed below.

Presentation by H. Bachimanchi at SPIE-ETAI, San Diego, 24 August 2022

Plankton tracking with holographic microscope and deep learning. (Image by H. Bachimanchi.)
Quantitative microplankton tracking by holographic microscopy and deep learning
Harshith Bachimanchi Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe
Presentation at SPIE-ETAI 2022
San Diego, USA
24 August 2022, 11:45 PDT

The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards 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 oceanic food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons through generations, continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data and allow inline 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. This method is particularly useful to explore the flux of carbon through microzooplankton, the most important and least known group of primary consumers in the global oceans. We exemplify this by detailed descriptions of microzooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 21-25 August 2022

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 21-25 August 2022, with the presentations listed below.

Giovanni Volpe is also co-author of the presentations:

Harshith Bachimanchi presented his half-time seminar on 10 May 2022

Harshith Bachimanchi’s half-time seminar. (Photo by Y.-W. Chang.)
Harshith Bachimanchi completed the first half of his doctoral studies and defended his half-time on 10th May 2022.

The presentation was held in hybrid format, with part of the audience present in the Nexus room and the rest connected through zoom. The half-time consisted of a presentation of his past and planned projects followed by discussion and questions proposed by his opponent Bernhard Mehlig.

The presentation started with a description of his project about combining holographic microscopy with deep learning to measure the dry mass and three-dimensional swimming patterns of marine microorganisms (Microplankton life histories revealed by holographic microscopy and deep learning). Thereafter, he discussed about some of the new experiments in marine microbial ecology where the technique is currently being used. In the last section, he outlined the proposed continuation of his PhD on studying active matter systems in marine microscopic environments using holographic microscopy and artificial neural networks.

Single-shot self-supervised particle tracking on ArXiv

LodeSTAR tracks the plankton Noctiluca scintillans. (Image by the Authors of the manuscript.)
Single-shot self-supervised particle tracking
Benjamin Midtvedt, Jesús Pineda, Fredrik Skärberg, Erik Olsén, Harshith Bachimanchi, Emelie Wesén, Elin K. Esbjörner, Erik Selander, Fredrik Höök, Daniel Midtvedt, Giovanni Volpe
arXiv: 2202.13546

Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on either vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, the data produced by experiments are often challenging to label and cannot be easily reproduced numerically. Here, we propose a novel deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And Regression), that learns to tracks objects with sub-pixel accuracy from a single unlabeled experimental image. This is made possible by exploiting the inherent roto-translational symmetries of the data. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we analyze challenging experimental data containing densely packed cells or noisy backgrounds. We also exploit additional symmetries to extend the measurable particle properties to the particle’s vertical position by propagating the signal in Fourier space and its polarizability by scaling the signal strength. Thanks to the ability to train deep-learning models with a single unlabeled image, LodeSTAR can accelerate the development of high-quality microscopic analysis pipelines for engineering, biology, and medicine.

Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion on ArXiv

Input graph structure including a redundant number of edges. (Image by J. Pineda.)
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo
arXiv: 2202.06355

The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here, we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically-relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments.