Invited Seminar by G. Volpe at Cognitive and Behavior Changes in Parkinson’s Disease and Parkinsonism: Advances and Challenges, Santa Maria di Leuca, Italy, 21 May 2025

Braph 2 Logo. (Image from the Braph 2 Project)
The Role of Artificial Intelligence in Advanced Neuroimaging Analysis
Giovanni Volpe
Cognitive and Behavior Changes in Parkinson’s Disease and Parkinsonism: Advances and Challenges
Date: 21 May 2025
Time: 11:50
Place: Tricase, Santa Maria di Leuca, Italy

Delayed Active Swimmer in a Velocity Landscape on ArXiv

Experimental setup. (Top) Thermophoretic microswimmer undergoes active Brownian motion in a spatially-varying laser intensity profile that controls the self-thermophoretic propulsion of the swimmer using a feedback loop. (Bottom) Sample trajectory of the microswimmer over 15 minutes in a chamber. Colors indicate instantaneous velocity. (Image from the manuscript.)
Delayed Active Swimmer in a Velocity Landscape
Viktor Holubec, Alexander Fischer, Giovanni Volpe, Frank Cichos
arXiv: 2505.11042

Self-propelled active particles exhibit delayed responses to environmental changes, modulating their propulsion speed through intrinsic sensing and feedback mechanisms. This adaptive behavior fundamentally determines their dynamics and self-organization in active matter systems, with implications for biological microswimmers and engineered microrobots. Here, we investigate active Brownian particles whose propulsion speed is governed by spatially varying activity landscapes, incorporating a temporal delay between environmental sensing and speed adaptation. Through analytical solutions derived for both short-time and long-time delay regimes, we demonstrate that steady-state density and polarization profiles exhibit maxima at characteristic delays. Significantly, we observe that the polarization profile undergoes sign reversal when the swimming distance during the delay time exceeds the characteristic diffusion length, providing a novel mechanism for controlling particle transport without external fields. Our theoretical predictions, validated through experimental observations and numerical simulations, establish time delay as a crucial control parameter for particle transport and organization in active matter systems. These findings provide insights into how biological microorganisms might use response delays to gain navigation advantages and suggest design principles for synthetic microswimmers with programmable responses to heterogeneous environments.

Aitor González Marfil joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Aitor González Marfil starts his visiting period at the Physics Department of the University of Gothenburg on 19 May 2025.

Aitor is a PhD student at the University of the Basque Country.

During his visit, that will last until the 19 of August, he will focus on machine learning for image analysis.

Robert Sosa Principe joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Robert Sosa Principe started his visit at the Physics Department at the University of Gothenburg on 17 May 2025.

Robert is a PostDoc in the group of Prof. Carlos Bustamante at the University of California, Berkeley.

During his visit, he will focus on experiments of single-molecule biophysics.

Invited talk by L. Viaene at the first PhD Conference at the University of Gothenburg, 25 April 2025

Linde Viaene presenting at the PhD conference. (Image by S. Kilde Westberg.)
Studying heat adaptation in yeast one-molecule at a time: The use of single-molecule microscopy for aggregate identification and tracking.

Linde Viaene
Date: 25th of April
Time: 13:00
Place: Veras Gräsmatta, Gothenburg

The importance of protein folding and misfolding is indicated by the broad range of clinical manifestations that have protein aggregation at the base, such as neurodegenerative diseases, cancer and type II diabetes. A key factor in (energy) homeostasis is the DNA configuration of chromatin which allows for essential gene expression and adaptation to environmental factors. The Rpd3 deacetylase histone complex (DHAC) plays a crucial role in gene regulation and its disruption impairs stress-induced gene activation, highlighting its importance in cellular adaptation.
Using Saccharomyces cerevisiae as a model system, we aim to investigate the role of chromatin remodelling components in protein aggregation and cellular rejuvenation, which may influence aggregate retention and recovery speed. We will expose yeast cells to stressors such as heat shock, metabolic shifts, and oxidative stress to assess their effects on protein homeostasis and chromatin regulation. Growth assays will evaluate survival rates, while Western blotting will measure Hsp104 expression, a key heat shock protein involved in aggregate clearance. By employing our bespoke single-molecule fluorescence microscope, we will track aggregate formation, clearance, and spatial localization in live cells at molecular precision.
Our preliminary results indicate that some components of the Rpd3L complex, respectively alter the recovery rate after heat stress exposure. Hence, the goal is to explore further candidate genes and to determine their role in the stress-induced response. By elucidating the role of chromatin remodelers in stress adaptation, our findings may inform novel therapeutic strategies for age-related diseases.

Invited Talk by G. Volpe at OPIC/OMC 2025, Yokohama, Japan, 21 April 2025 (Online, Pre-recorded)

DeepTrack 2 Logo. (Image from DeepTrack 2 Project)
How can deep learning enhance microscopy?
Giovanni Volpe
Optics & Photonics International Congress 2025 (OPIC 2025), The 11th Optical Manipulation and Structured Materials Conference (OMC2025)
Date: 21 April 2025
Time: 13:45 JST
Place: Yokohama, Japan (Online, Pre-recorded)

Computational memory capacity predicts aging and cognitive decline published in Nature Communications

Memory capacity in aging. A Brain reservoir computing architecture with uniform random signals applied to all nodes. (Image from the article.)
Computational memory capacity predicts aging and cognitive decline
Mite Mijalkov, Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira
Nature Communications 16, 2748 (2025)
doi: 10.1038/s41467-025-57995-0

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

Ade Satria Saloka Santosa joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Ade Satria Saloka Santosa joins the Physics Department of the University of Gothenburg as a visiting PhD student from Uppsala University on 1 February 2025.

Ade holds a Master of Science degree in Industrial Chemistry from Pukyong National University, South Korea, and has research experience at the Korea Institute of Materials Science (KIMS).

During his PhD, he will focus on nanofabrication and e-paper technology.

Mathilda Gustafsson joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Mathilda Gustafsson joined the Soft Matter Lab on 20 January 2025.

Mathilda is a master student in Complex Adaptive Systems at Chalmers University of Technology.

During her time at the Soft Matter Lab, she will work on a project about tracking bacteria in sequences of microscopic images. In particular she will try to solve problems with overlapping bacteria using recurrent neural networks.