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.

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.

Harshith Bachimanchi won best early career researcher presentation award at ETAI 2023, San Diego

The three award winners. From left to right: Mite Mijalkov, Harshith Bachimanchi, Marie Drouhin. (Photo by G. Volpe.)
Harshith Bachimanchi won the best early career researcher presentation (gold) award at Emerging Topics in Artificial Intelligence (ETAI) 2023 held in San Diego, California, USA, from 20 – 24 August 2023. The award, consisting of an invitation to a part of a perspective article of AI in neurosciences, is offered by the organisers of the conference, and SPIE Optics + Photonics.

In this work, Harshith presented his recent work on combining holographic microscopy and deep learning to study the marine microplankton. He demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. This enables 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. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. He exemplified this by showing 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.

Award certificate of Harshith Bachimanchi. (Provided by H. Bachimanchi.)
Harshith Bachimanchi receives the award from Joana B. Pereira. (Photo by G. Volpe.)
The three award winners. From left to right: Mite Mijalkov, Harshith Bachimanchi, Marie Drouhin. (Photo by G. Volpe.)

Presentation by H. Bachimanchi at SPIE-ETAI, San Diego, 23 August 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Decoding microplankton life through holographic microscopy and deep learning
Harshith Bachimanchi
23 August 2023, 8:45 AM PDT

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 H. Bachimanchi at XVII International Congress of the Spanish Biophysical Society, Castelldefels, 30 June 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Bringing microplankton into focus: Deep learning meets holographic microscopy
Harshith Bachimanchi
30 June 2023, 12:40 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 H. Bachimanchi at AI for Scientific Data Analysis, Gothenburg, 1 June 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Quantitative microplankton tracking by holographic microscopy and deep learning
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 H. Bachimanchi at Signals in the Sea mini-symposium, Lund University, 12 May 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)
Deep learning in plankton ecology
Harshith Bachimanchi
Presentation at Biology department, Lund University
12 May 2023, 13:00 CEST

In this mini-symposium organised by Dr. Erik Selander at Lund University, I have spoken about our recent work with marine microplankton, where we have combined holographic microscopy with deep learning to measure the ‘dry’ mass and 3D swimming dynamics of different species of planktons. The article related to this presentation can be found at the following here: Microplankton life histories revealed by holographic microscopy and deep learning.

The presentation was followed by discussions with Prof. Karin Rengefors group at Lund university on the topic of application of AI based methods for various kinds of studies in phytoplankton ecology and evolution.

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)

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.