Tag: Giovanni Volpe

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 23-27 August 2026

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 23-27 August 2026, with the presentations listed below.

Giovanni Volpe is a coauthor of the following presentations:

  • Mite Mijalkov In-silico cognition signals aging and cognitive decline
    23 August 2026 • 3:15 PM – 3:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Iok-ui Tiunn Unsupervised analysis of 3D human bone reveals age-sensitive niches for CXCL12+ stromal cells
    24 August 2026 • 2:15 PM – 2:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Alexandra Badea Graph-Based Embeddings of Laminar Cortical Columns Reveal Interpretable Microstructural Patterns Associated with Alzheimer’s Disease Risk (Invited Paper)
    26 August 2026 • 10:30 AM – 11:00 AM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Massimiliano Passaretti Clinical progression and genetic pathways in body-first and brain-first Parkinson’s disease
    26 August 2026 • 2:15 PM – 2:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Mite Mijalkov The locus coeruleus gap: a novel marker of neurodegenerative resilience
    27 August 2026 • 8:45 AM – 9:00 AM PDT | Conv. Ctr. Room 2
    [link on SPIE]

Invited Presentation by G. Volpe at SPIE-Ultrafast Nonlinear Imaging and Spectroscopy XIV, 24 August 2026

DeepTrack 2 Logo. (Image from DeepTrack 2 Project)
Artificial Intelligence for Microscopy: From Pixels to Physical Insight
Giovanni Volpe
(Invited Paper)
Date: 24 August 2026
Time: 9:45 AM – 10:10 AM PDT
Place: Conv. Ctr. Room 15B

Advances in artificial intelligence are transforming microscopy from a tool of observation into a tool of discovery. Modern machine learning algorithms—especially deep neural networks—enable us to reconstruct, segment, and interpret microscopic images with unprecedented speed and precision. In this talk, I will discuss how AI can enhance every step of the microscopy pipeline, from data acquisition and noise reduction to virtual staining and quantitative analysis. Drawing on examples from my group’s work—including the DeepTrack framework for physics-aware deep learning and AI-driven virtual microscopy—I will show how we can train models to reveal hidden structures, track dynamic processes, and even uncover underlying physical laws directly from images. Beyond technical performance, I will also address the broader implications of this transformation: how AI reshapes the role of the experimenter, the reproducibility of quantitative microscopy, and the future of scientific understanding in an era of data-driven discovery.

EquiNET: A new technique for estimating Equilibrium free-energy differences from Non-Equilibrium Trajectories on arXiv

EquiNET uses a graph neural network to infer entropy production from non-equilibrium molecular trajectories and uses this information to estimate equilibrium free-energy differences. (Image by S. K. Manikandan.)
Free-Energy Differences from Nonequilibrium Fluctuations in High Dissipation
Sreekanth K Manikandan and Giovanni Volpe
arXiv: 2608.23394

Equilibrium free-energy differences can be measured from nonequilibrium work fluctuations using fluctuation theorems, such as the Jarzynski equality and the Crooks fluctuation theorem. However, these approaches become statistically inefficient in high-dissipation regimes because their convergence requires trajectories with negative entropy production, events that occur exponentially rarely. Here, we demonstrate that repeated measurements of trajectory fluctuations are sufficient to determine entropy production without relying on such rare events, or to determine a lower bound on it when only partial measurements are available. This information then yields exact estimates of free-energy differences, or rigorous bounds. We validate this approach, named EquiNET, with numerical simulations, including one of biomolecular folding and unfolding, showing that it recovers accurate free-energy estimates even in regimes where conventional approaches fail.

Poster presentation by G. Volpe at SPIE-ETAI, San Diego, 24 August 2026

An energy- and data-efficient foundation model for high-content phenotypic screening
Benjamin Midtvedt, Jesús D. Pineda Castro, Henrik Klein Moberg, Jim Denholm, Mattias Goksör, Christos Matsoukas, Bjarki Johannesson, Giovanni Volpe
Date: 24 August 2026
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall B1

High-content screening (HCS) enables large-scale characterization of cellular responses to chemical and genetic perturbations, yet current deep learning approaches remain limited by label scarcity, batch effects, and high computational cost. We present an energy- and data-efficient foundation model for high-content phenotypic screening that unifies these challenges within a single architecture. Trained on the full JUMP Cell Painting dataset, the model combines active learning with contrastive supervision to focus representation learning on biologically informative variation while reducing sensitivity to technical artifacts. By supporting multimodal integration of molecular metadata alongside fluorescence morphology, the model learns generalizable cellular representations that transfer across experimental conditions and downstream phenotypic tasks.

Keynote presentation by G. Volpe at SPIE-MNM, San Diego, 23 August 2026

Top: single gear; Bottom: the second gear from the right has an optical metamaterial that react to laserlight and makes the gear move. All gears are made in silica directly on a chip. Each gear is about 0.016 mm in diameter. (Image by G. Wang)
Light-driven metamachines: From metarotors to integrated microscopic gear trains
(Keynote Presentation)
Giovanni Volpe
Date: 23 August 2026
Time: 8:30 AM – 9:05 AM PDT
Place: Conv. Ctr. Room 11A

Microscopic geared metamachines provide a powerful platform to control motion and mechanical work at the microscale. While the miniaturization of mechanical systems has been pursued for decades, integrating drives and gear trains at micrometer scales has remained a major challenge. Recent advances in optical metasurfaces and on-chip fabrication enable light-driven metarotors that transfer torque to passive gears under uniform illumination. These systems have been explored under various conditions by varying light polarization, intensity, and structural symmetry to achieve directional control, tunable rotation rates, and efficient torque transmission. Integrated designs such as rack-and-pinion configurations further demonstrate conversion of rotational motion into linear actuation within compact microsystems. More broadly, geared metamachines extend the landscape of light-powered micro- and nanomachines. In this presentation, I’ll give an overview of this field with a focus on metarotor design, torque transfer across microscopic gear trains, and integrated light-programmable micromechanical systems.

Global graph features unveiled by unsupervised deep learning published in Machine Learning: Science and Technology

GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)
Global graph features unveiled by unsupervised deep learning
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
Machine Learning: Science and Technology, 7, 045054 (2026)
arXiv: 2503.05560
doi: 10.1088/2632-2153/ae8d7f

Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce Graph Autoencoder Uncovering Descriptive Information (GAUDI), an unsupervised graph deep learning framework designed to capture both local details and global structure. GAUDI employs an hourglass architecture with hierarchical pooling and upsampling layers linked through skip connections, which preserve essential connectivity information throughout the encoding–decoding process. Even though identical or highly similar underlying parameters describing a system’s state can lead to significant variability in graph realizations, GAUDI consistently maps them into nearby regions of a structured and continuous latent space, effectively disentangling invariant process-level features from stochastic noise. We demonstrate GAUDI’s versatility across multiple applications, including small-world networks modeling, characterization of protein assemblies from super-resolution microscopy, analysis of collective motion in the Vicsek model, and identification of age-related changes in brain connectivity. Comparison with related approaches highlights GAUDI’s superior performance in analyzing complex graphs, providing new insights into emergent phenomena across diverse scientific domains.

Vulnerability of locus coeruleus connections to aging and Alzheimer’s disease published in Alzheimer’s & Dementia

Anatomical gradients of the right (R) and left (L) LC in the AD continuum cohort (ADNI). (Image from the article.)
Vulnerability of locus coeruleus connections to aging and Alzheimer’s disease
Blanca Zufiria-Gerbolés, Daniel Vereb, Mite Mijalkov, Massimiliano Passaretti, Giovanni Volpe, Zhilei Xu, Thomas Hinault, Sara Garcia-Ptacek, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71737 (2026)
DOI: 10.1002/alz.71737

INTRODUCTION
The locus coeruleus (LC) is important in coordinating communication between brain regions through its widespread connections. However, the organization of its connections, and its changes with aging and Alzheimer’s disease (AD), remains unclear.

METHODS
We mapped whole-brain white matter connections from the LC in two independent cohorts: one spanning the adult lifespan and another covering the AD continuum.

RESULTS
We identified a novel dorsal–ventral organization of LC connectivity that showed changes common to aging and AD or specific to AD, was linked to gene expression patterns, and was associated with cognitive performance and tau pathology in the entorhinal cortex. We also developed an imaging marker, LC gap, capturing LC connectivity deviations associated with slower tau accumulation in cognitively normal individuals at high risk for AD.

DISCUSSION
These findings provide new insights into LC connectivity in aging and AD, highlighting its potential role as a resilience marker against AD neurodegeneration.

Highlights

  • For the first time, we mapped the whole-brain white matter connections from the Locus Coeruleus (LC) to the rest of the brain across aging and Alzheimer’s disease (AD) continuum.
  • Revealed a novel dorsal–ventral organization LC connectivity that presents changes common to aging and AD or specific to AD.
  • LC connectivity patterns are associated with cognitive performance and tau pathology in the entorhinal cortex.
  • We introduced a novel marker capturing LC connectivity deviations and that is associated with tau accumulation in cognitively normal individuals at high risk for AD.

Plenary presentation by G. Volpe at the MüSIM, Münster, Germany, 24 June 2026.

Illustration of three different experiments autonomously performed by the SmartTrap system: DNA pulling experiments (top), red blood cell stretching (bottom left), and particle-particle interaction measurements (bottom right). (Image by M. Selin.)
Smart Machines and Optical Manipulation at the Microscale
Giovanni Volpe
5th Münster Symposium on Intelligent Matter (MüSIM) (Flyer)
Date: 24 June 2026
Time: 15:00
Place: Center for Soft Nanoscience (SoN), Münster, Germany

Microscale systems offer a unique opportunity to engineer machines whose function emerges from the interplay of geometry, interactions, and fluctuations. In this talk, I will present our work on smart machines at the microscale, combining nanofabrication, programmable interactions, and advanced optical methods to design and control colloidal micromechanisms and metamaterials.
I will first introduce how nanotechnology enables the realization of colloidal metamachines and microscopic mechanisms, where shape and mechanical constraints are engineered to produce targeted motion and response in fluid environments. I will then show how smart microscopy and optical manipulation—including high-resolution imaging, automated tracking, and light-based control—allow us to probe these machines in real time and quantify their dynamics. This approach enables precision measurements of effective interaction landscapes, including critical Casimir forces and their relation to fluctuation-induced forces known from QED Casimir physics.

SmartTrap: automated precision experiments with optical tweezers published in Nature Methods

Illustration of three different experiments autonomously performed by the SmartTrap system: DNA pulling experiments (top), red blood cell stretching (bottom left), and particle-particle interaction measurements (bottom right). (Image by M. Selin.)
SmartTrap: automated precision experiments with optical tweezers
Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, Giovanni Volpe
Nature Methods (2026)
arXiv: 2505.05290
doi: 10.1038/s41592-026-03129-3

Optical tweezers are widely used in single-molecule biophysics, cell biomechanics and soft matter physics, but require a human operator, limiting throughput and repeatability. Here we present a smart optical tweezers platform, named SmartTrap, capable of performing complex experiments autonomously by integrating real-time three-dimensional particle tracking, custom electronics and a microfluidics system. Through a series of experiments, we demonstrate it can operate continuously, acquiring high-precision data over extended periods of time. By bridging the gap between manual experimentation and autonomous operation, SmartTrap establishes a robust and open-source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics and colloidal science with minimal experimental overhead and operator bias.

Protein Dynamics Beyond Structure Prediction on ArXiv

Computational advances in protein folding studies. Current approaches address multiple levels of resolution and methodological frameworks, however, none of the existing methods provides quantitative and dynamic information of the relationship between protein sequence and folding mechanism at all-atom resolution and at scale. (Graphics by J. Sacquegno.)
Protein Dynamics Beyond Structure Prediction
Juliette Griffié, Sviatlana Shashkova, Antonio Ciarlo, Sreekanth K. Manikandan, Claes Andréasson, Malin Bäckström, Tristan Bereau, Hjalmar Brismar, Carlos Bustamante, Marta Carroni, Roberto Covino, Andreas Dahlin, Sebastian Deindl, Lucie Delemotte, Arne Elofsson, John Eriksson, Giovanna Fragneto, Anders Gunnarsson, Per Hammarström, Caroline Ingre, Christian Kaiser, Petronella Kettunen, Mark C. Leake, Benjamin Loos, Anna Månberg, Antonia S. J. S. Mey, Richard Neutze, Thomas Nyström, Karl Palmås, Charley Schaefer, Markus J. Tamás, Nicola Ticozzi, Tomás S. Pilvelic, Jacopo Sacquegno, B.M. (Betty)Tijms, Gunnar von Heijne, Björn Wallner, Vitali Zhaunerchyk, Simon Olsson, Joana B. Pereira, Julia Fernandez-Rodriguez, Fredrik Westerlund, Giovanni Volpe
arXiv: 2606.08647

How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy — for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior — and thus opening new avenues for research in complex biological and ecological systems.