Soft Matter Lab

Deep-learning-powered data analysis in plankton ecology published in Limnology and Oceanography Letters

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
Limnology and Oceanography Letters (2024)
doi: 10.1002/lol2.10392
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 phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. 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.

Emiliano Gómez will defend his PhD thesis on 22 May 2024

Emiliano Gómez will defend his PhD thesis on the 22th of May at 10:30. The defense will take place in KA (Chemistry Department, Johanneberg Campus)

Title: Development and Application of a software to analyse networks with multilayer graph theory and deep learning

Abstract:
Network theory gives us the tools necessary to produce a model of our brain, how the brain is wired will give us a new level of insight into its functionality. The brain network, the connectome, is formed by structural links such as synapses or fiber pathways in the brain. This connectome might also be interpreted from a statistical relationship between the flow of information, or activation correlation between brain regions. Mapping these networks can be achieved by using neuroimaging, which allows obtaining information on the brain in vivo. Different neuroimaging modalities will capture different properties of the brain. Statistical analysis is necessary for extracting meaningful insights regarding the network patterns obtained from neuroimages. For this, huge data banks are a byproduct of the need for enough data to be able to tackle medical and biological questions.

In this work, we present a software “Brain Analysis using Graph Theory 2” (BRAPH 2) (Paper I), which addresses the need for a toolbox designed for both complex graph theory and deep learning analyses of different imaging modalities. With BRAPH 2, we offer the neuroimaging community a tool that is open-source, flexible, and intuitive. BRAPH 2, at its core, comes with multi-graph capabilities. For Paper II, we employed the power of multiplex and multigraph capabilities of BRAPH 2 to analyze sex differences in brain connectivity for an aging healthy population. Finally, for Paper III, BRAPH 2 has been adapted to two new graph measures (global memory capacity, and nodal memory capacity), which obtain a prediction of memory capacity using Reservoir Computing and relate this new measure to biological and cognitive characteristics of the cohort.

Supervisor: Giovanni Volpe
Examiner: Raimund Feifel
Opponent: Saikat Chatterjee, KTH, Stockholm
Committee: Marija Cvijovic, Alireza Salami, Wojciech Chachólski
Alternate board member: Mats Granath

CHAIR seminar by C. Martinez on 27 March 2024

Introduction to G-Research, a quantitative research and technology company
Charles Martinez
G-Research, London, UK
27 March 2024
12:30-14:30
PJ
Organized by the CHAIR theme AI for Scientific Data Analysis

Bio: Dr Charles Martinez is the Academic Relations Manager at G-Research. Charles started his studies as a physicist at University Portsmouth Physics department’s MPhys programme, and later completed a PhD in Phonon interactions in Gallium Nitride nanostructures at the University of Nottingham. Charles then worked on indexing and abstract databases at the Institution for Engineering and Technology (IET) before moving into sales in 2010. Charles’ previous role was as Elsevier’s Key Account Manager, managing sales and renewals for the UK Russell Group institutions, Government and Funding body accounts, including being one of the negotiators in the recent UK ScienceDirect Read and Publish agreement. Since leaving Elsevier Charles is dedicated to forming beneficial partnerships between G-Research and Europe’s top institutions, and is living in Cambridge, UK.

Seminar by C. Martinez on 27 March 2024

Learning about G-Research: thinking about strategies in quantitative finance
Charles Martinez
G-Research, London, UK
27 March 2024
10:00-11:30
FB (Fysik-Huset)

We are a leading quantitative research and technology company based in London. Day to day we use a variety of quantitative techniques to predict financial markets from large data sets worldwide. Mathematics, statistics, machine learning, natural language processing and deep learning is what our business is built on. Our culture is academic and highly intellectual. In this seminar I will explain our background, current AI research applications to finance and our ongoing outreach, recruitment and grants programme. The seminar will be aimed at students who are curious about quant finance or interested in internship opportunities. We will also play an interactive game. The game will last around 1 hour and there will be prizes for the Top 3 scores (amazon vouchers – £100). Dice will be provided.

Seminar by W. Ahmed on 13 March 2024

A schematic of a passive particle immersed in an active bath experiencing non-equilibrium fluctuations. *(Illustration by W. Ahmed)*

Emergent behavior in active biological matter
Wylie Ahmed
Laboratoire de Physique Theorique, Toulouse (France) and California State University, Fullerton (USA)

13 March 2024, 12:30, Nexus

Motivated by nucleus centering in mouse oocytes, we explore a different type of biological active matter. We investigate the stochastic force fluctuations of micro swimmers in two scenarios: (1) a single swimmer navigating through a passive fluid; (2) a dense suspension of swimmers surrounding a passive tracer. By direct force measurement using optical tweezers we show that the force trajectory of an individual micro swimmer exhibits rich oscillatory dynamics that vary in time. Interestingly, when these highly fluctuating force dynamics are analyzed using the framework of stochastic thermodynamics we recover energy dissipation rates in agreement with time-averaged fluid dynamics studies. For a dense suspension of swimmers serving as an active bath for a passive tracer we observe both shear thinning and thickening, which depends on Peclet number, and enhanced diffusion of our tracer by a factor of 2. We estimate the energy transfer rate from the active bath to the passive tracer. These two scenarios allow us to explore energy exchange between an active swimmer in a passive bath and a passive tracer in an active bath.

Berenice García Rodríguez presented her half-time seminar on 8 March 2024

Berenice García Rodríguez (right) and opponent Dr. Hana Jungová (left). *(Photo by J. P. Ramírez)*

Berenice García Rodríguez completed the first half of her doctoral studies, and she defended her half-time on the 8th of March 2024.

The presentation, “Quantitative Analysis of Nanoparticle Properties Using Optical Scattering Techniques,” was held in a hybrid format, with part of the audience in the Nexus room and the rest connected through Zoom. The half-time consisted of a presentation about her past and planned projects, followed by a discussion and questions proposed by her opponent, Dr. Hana Jungová.

The presentation started with a short background introduction to optical scattering techniques and nanoparticle characterization techniques, followed by an introduction and description of the first paper, “Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization,” and, in the end, a brief description of the projects in which Berenice is involved.

In the last section, she outlined the proposed continuation of her PhD: quantification and characterization of biomolecular condensates and their evolution over time, monitoring lipid droplets during long timescales inside living cells, and parametrization for core-shell particles.

Giovanni Volpe awarded the Göran Gustafsson prize

Giovanni Volpe was awarded one of Sweden’s most prestigious prizes for physics, the Göran Gustafsson Prize, which is handed out by the Göran Gustafsson Foundation with the help of the Royal Swedish Academy of Sciences. Giovanni receives the physics prize “for boundary breaking research focusing on microscopic particles with active functions”. The prize sum is 6.3 million SEK.

More details here:
Press release of Gothenburg University: Giovanni Volpe receives prestigious Göran Gustafsson prize
Press release of Kungl. Vetenskapsakademien: 33 miljoner till forskning om bland annat TBE och smarta mikropartiklar

Wylie Ahmed visits the Soft Matter Lab. Welcome!

Wylie Ahmed is a Visiting Professor from the Laboratoire de Physique Theorique in Toulouse, France. He is also an associate professor (on leave) at California State University, Fullerton where he leads the Laboratory for Soft, Living, and Active Matter (SLAMLab). His visiting position is financed through the CNRS with partial support from the Soft Matter Lab.
He will visit us for 5 months from March 1, 2024, to July 31, 2024.

He completed his Ph.D. at the University of Illinois at Urbana-Champaign, and was a Marie Skłodowska-Curie Research Fellow at the Institut Curie in Paris, France. He started his group in 2016 in California and is now moving his research activities to Toulouse France. His research interests are in cellular biophysics, soft and active matter physics, and bio-inspired materials with a theme towards understanding emergent behavior.

Anoop C. Patil joins the Soft Matter Lab

Anoop C. Patil joined the Soft Matter lab on March 1, 2024.

Anoop is a Senior Fellow at the Singapore-MIT Alliance for Research & Technology (SMART) center, based in the National University Singapore campus, Singapore.

He is working on computational analysis for precision agriculture at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), SMART, Singapore. As a part of this work, he is also working on the BRAPH-2 platform for spectral analysis applications at DiSTAP, SMART, and Temasek Life Sciences Laboratory (TLL), Singapore.

Invited Talk by Yu-Wei Chang in the group meeting of Prof. Michael Strano, Department of Chemical Engineering, Massachusetts Institute of Technology, USA, 23 Feb 2024

Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) *(Image by Yu-Wei Chang.)*

GapNet: Neural network training with highly incomplete datasets

Yu-Wei Chang

Presentation in group meeting of Prof. Michael Strano, Department of Chemical Engineering, Massachusetts Institute of Technology, USA and DiSTAP, Singapore-MIT Alliance for Research and Technology, Singapore.
Date: 23 February 2024

Neural network training requires complete data. We have introduced GapNet, which can train neural networks with incomplete data, using medical data. This approach can be generalized for integrating spectrum data across different frequency ranges, allowing the neural network to combine important information from diverse spectrum datasets.