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Yu-Wei Chang nailed his PhD thesis on January 7th, 2026. Congrats!

Thesis nailing by Yu-Wei Chang. (Photo by C. Khanolkar.)
Yu-Wei Chang nailed his PhD thesis, A Unified Software-Generating Framework for Biological Data Analysis, on January 7th, 2026. Congrats!

The nailing took place in Universitetsbyggnaden i Vasaparken, Universitetsplatsen 1, Göteborg, at 13:30.

In Swedish academia, “nailing” (spikning) is the formal public announcement and publication of a doctoral thesis. It happens weeks before the defence so that the public has time to read the thesis in advance and prepare questions for the defence. In addition to the physical nailing, the thesis is also published electronically (e-spikning) via GUPEA.

Yu-Wei will defend his thesis on 23 January at 13:00 in SB-H7 lecture hall, SB-Building, Institutionen för fysik, Johanneberg Campus, Göteborg.

Thesis (GUPEA handle): http://hdl.handle.net/2077/90289

Thesis Nailing by B. García Rodríguez, 7 January 2026. Congrats!

Thesis nailing by Berenice García Rodríguez. (Photo by C. Khanolkar.)
On 7 January at 13:00, Berenice García Rodríguez nailed her PhD thesis, Quantitative Optical Microscopy of Microscale Soft Matter Systems, at the University of Gothenburg in Vasaparken.
Family and friends were present to mark this milestone.

Berenice will defend her thesis on 28 January at 09:00 in PJ-salen, Institutionen för fysik, Origovägen 6, Göteborg.

Thesis Nailing by F. Skärberg, 7 January 2026. Congrats!

Thesis nailing by Fredrik Skärberg. (Photo by E. Skärberg.)
On 7 January at 12:30, Fredrik Skärberg nailed his PhD thesis, From Light to Data: Using Deep Learning for Quantitative Microscopy, at the University of Gothenburg in Vasaparken. Family and friends were present to mark this milestone.

Fredrik will defend his thesis on 29 January at 09:00 in FB-salen, Institutionen för fysik, Origovägen 6, Göteborg.

Inchworm-Inspired Soft Robot with Groove-Guided Locomotion on ArXiv

Photograph of the soft robot, consisting of a multilayer rolled dielectric elastomer actuator integrated with a
flexible PET sheet. (Image by H. P. Thanabalan.)
Inchworm-Inspired Soft Robot with Groove-Guided Locomotion
Hari Prakash Thanabalan, Lars Bengtsson, Ugo Lafont, Giovanni Volpe
arXiv: 2512.07813

Soft robots require directional control to navigate complex terrains. However, achieving such control often requires multiple actuators, which increases mechanical complexity, complicates control systems, and raises energy consumption. Here, we introduce an inchworm-inspired soft robot whose locomotion direction is controlled passively by patterned substrates. The robot employs a single rolled dielectric elastomer actuator, while groove patterns on a 3D-printed substrate guide its alignment and trajectory. Through systematic experiments, we demonstrate that varying groove angles enables precise control of locomotion direction without the need for complex actuation strategies. This groove-guided approach reduces energy consumption, simplifies robot design, and expands the applicability of bio-inspired soft robots in fields such as search and rescue, pipe inspection, and planetary exploration.

MSCA-DN GREENS training event in Göteborg, 10-14 November 2025

GREENS Logo. (Image from MSCA-DN GREENS)
The Soft Matter Lab hosted the first training event of the MSCA-DN GREENS from the 10th to the 14th of November 2025.
Both Andrea Schiano di Colella and Eduard Andrei Duta Costache, the two doctoral candidates based at the University of Gothenburg, participated to the event along with the other doctoral candidates of the network.

The training event consisted of a series of lectures on different topics such as small scale robots and their actuation mechanisms, theoretical aspects of the simulation of the dynamics of bodies immersed in fluids at low Reynolds numbers, working principles and applications of swarm robotics, and finally artificial intelligence and its applications to data analysis in experiments.

The event included a visit to the Swedish Algae Factory (SAFAB) and Smogenlax on the topic of circular economy, and concluded with a series of meetings between the representatives of the participating institutions, from both accademia and industry, to exchange research questions and plan secondments.

Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!

Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!
The defense took place in SB-H7 lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 9:00.

Title: Inductive Biases for Efficient Deep Learning in Microscopy

Abstract: Deep learning has become an indispensable tool for the analysis of microscopy data, yet its integration into routine research remains uneven. Several factors contribute to this gap, including the limited availability of well-annotated datasets and the high computational demands of modern architectures. Microscopy introduces further challenges, as it spans diverse modalities and scales, from proteins to tissues, producing heterogeneous data that defy standardization. Generating reliable annotations also requires expertise and time, while unequal access to high-performance computing further widens the divide between well-resourced institutions and smaller laboratories.

This dissertation argues that the prevailing paradigm of scaling models with ever-larger datasets and computational resources yields diminishing returns for microscopy. Instead, it explores the role of inductive biases as a foundation for building models that are more data-efficient, computationally accessible, and scientifically meaningful. Inductive biases are structural assumptions embedded in model design that guide learning toward patterns aligned with the underlying problem. The first part of this work examines their central role in the advancement of modern deep learning and the diverse ways they shape model behavior.

This potential is demonstrated through three case studies. First, MAGIK employs graph neural networks to analyze biological dynamics in time-lapse microscopy, uncovering local and global properties with high precision, even when trained on limited data. Next, MIRO leverages recurrent graph neural networks to process single-molecule localization datasets, improving the efficiency and reliability of clustering for variable biological structures and scales while retaining strong generalization with minimal supervision. Finally, GAUDI introduces a representation learning framework for characterizing biological systems, providing a physically meaningful representation space for interpretable and transferable analysis.

The findings presented here demonstrate that the integration of inductive biases provides a cohesive strategy to extend the reach of deep learning in the life sciences, enhancing accessibility and ensuring scientific utility under resource constraints.

Thesis: https://gupea.ub.gu.se/items/672c7946-51d6-4773-ad8c-35a3eed41499

Supervisor: Giovanni Volpe
Co-Supervisor: Carlo Manzo
Examiner: Raimund Feifel
Opponent: Anna Kreshuk
Committee: Juliette Griffié, Daniel sage, Daniel Persson
Alternate board member: Jonas Enger

Enhanced spatial clustering of single-molecule localizations with graph neural networks published in Nature Communications

MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)
Enhanced spatial clustering of single-molecule localizations with graph neural networks
Jesús Pineda, Sergi Masó-Orriols, Montse Masoliver, Joan Bertran, Mattias Goksör, Giovanni Volpe and Carlo Manzo
Nature Communications 16, 9693 (2025)
arXiv: 2412.00173
doi: 10.1038/s41467-025-65557-7

Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.

How Do Proteins Fold? on ArXiv

Conceptual models of protein folding: funnel versus foldon. (Figure from the Authors of the manuscript.)
How Do Proteins Fold?
Carlos Bustamante, Christian Kaiser, Erik Lindahl, Robert Sosa, Giovanni Volpe
arXiv: 2510.27074

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate folding pathways. Two main conceptual models dominate as explanations of folding mechanism: the funnel model, in which folding proceeds through many alternative routes on a rugged, hyperdimensional energy landscape; and the foldon model, which proposes a hierarchical sequence of discrete intermediates. Recent advances on two fronts are now enabling folding studies in unprecedented ways. Powerful experimental approaches; in particular, single-molecule force spectroscopy and hydrogen (deuterium exchange assays) allow time-resolved tracking of the folding process at high resolution. At the same time, computational breakthroughs culminating in algorithms such as AlphaFold have revolutionized static structure prediction, opening opportunities to extend machine learning toward dynamics. Together, these developments mark a turning point: for the first time, we are positioned to resolve how proteins fold, why they misfold, and how this knowledge can be harnessed for biology and medicine.

Myxococcus xanthus for active matter studies: a tutorial for its growth and potential applications published in Soft Matter

Myxococcus xanthus colonies develop different strategies to adapt to their environment, leading to the formation of macroscopic patterns from microscopic entities. (Image by the Authors of the manuscript.)
Tutorial for the growth and development of Myxococcus xanthus as a Model System at the Intersection of Biology and Physics
Jesus Manuel Antúnez Domínguez, Laura Pérez García, Natsuko Rivera-Yoshida, Jasmin Di Franco, David Steiner, Alejandro V. Arzola, Mariana Benítez, Charlotte Hamngren Blomqvist, Roberto Cerbino, Caroline Beck Adiels, Giovanni Volpe
Soft Matter 21, 8602-8623 (2025)
arXiv: 2407.18714
doi: 10.1063/5.0235449

Myxococcus xanthus is a unicellular organism known for its capacity to move and communicate, giving rise to complex collective properties, structures and behaviors. These characteristics have contributed to position M. xanthus as a valuable model organism for exploring emergent collective phenomena at the interface of biology and physics, particularly within the growing domain of active matter research. Yet, researchers frequently encounter difficulties in establishing reproducible and reliable culturing protocols. This tutorial provides a detailed and accessible guide to the culture, growth, development, and experimental sample preparation of M. xanthus. In addition, it presents several exemplary experiments that can be conducted using these samples, including motility assays, fruiting body formation, predation, and elasticotaxis—phenomena of direct relevance for active matter studies.

Andrea Schiano di Colella joins the Soft Matter Lab

Andrea Schiano di Colella. (Photo by A. Ciarlo.)
Andrea Schiano di Colella started his PhD at the Physics Department of the University of Gothenburg on the 24th of October 2025.

Andrea has a Master degree in Theoretical Physics from the University of Naples “Federico II”, Italy.

During the course of his PhD, as part of the GREENS MSCA Doctoral Network he will focus on the development of deep learning based protocols for accurate autonomous microscopy.