Category: Presentation

Invited Talk by S. K. Manikandan at Chalmers Condensed Matter Seminar Series

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.)

Nonequilibrium Fluctuations as Probes of Thermodynamics
Date: 6 October 2026
Time: 11:00
Place: PJ seminar room, Fysik Origo

The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.

Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for overcoming these challenges. In this talk, I will first introduce our theoretical results showing how the time dependence of dynamical fluctuations of a system (due to interactions with its environment) can be exploited to quantify dissipation at scales inaccessible to calorimetry. I will then demonstrate how these measurements can be used to address a long-standing challenge related to inferring equilibrium free-energy differences from nonequilibrium measurements. The results will be illustrated using experiments and simulations of biophysical systems, demonstrating both the practical applicability of the method and the challenges that remain.

Presentation by J. Tember at Scintille di Scienza, Pisa, 25 September 2026

FIREFLY provides interactive rendering, exploration and analysis of large three-dimensional light-sheet microscopy datasets, combining real-time visualization, volumetric data processing and machine learning-based approaches for improved microscopy image analysis. (Image by J. Tember)
FIREFLY: Framework for Interactive Rendering, Exploration and Feature Extraction of Light-Sheet Microscopy
John Tember
Date: 25 September 2026
Time: 18:00 – 18:15
Place: Largo Ciro Menotti, Pisa, Italy

Modern tissue-clearing and light-sheet microscopy techniques can generate highly detailed three-dimensional images of biological samples, but the increasing size and complexity of these datasets pose significant challenges for efficient visualization, processing and analysis. Interactive computational tools are therefore essential for exploring large volumetric datasets and extracting quantitative information from them.

FIREFLY is being developed as a framework for interactive rendering, exploration and feature extraction of light-sheet microscopy data. The presentation introduced computational approaches for real-time 3D visualization, volumetric data processing and multi-scan reconstruction, together with ongoing work using self-supervised machine learning to improve the resolution of anisotropic microscopy volumes.

The work was presented at Scintille di Scienza: la Ricerca dal Laboratorio alla Piazza – II Edizione, a public outreach event organized by the Department of Pharmacy at the University of Pisa as part of BRIGHT Night 2026, the European Researchers’ Night in Tuscany. The event brought researchers to the city centre of Pisa for short talks, interactive activities and demonstrations aimed at communicating current research to a broad public audience.

The aim of FIREFLY is to develop computational tools that make increasingly large and complex microscopy datasets easier to explore, process and quantitatively analyse.

Invited Talk by G. Volpe at the TR+ Conference 2026: From Static to Dynamics, LINXS, Lund, 21 September 2026

Conceptual models of protein folding: funnel versus foldon. (Figure from the Authors of the manuscript.).

How do proteins fold?
Date: 21 September 2026
Time: 13:15
Place: LINXS at The Loop, Lund

Giovanni Volpe will give the invited talk “How do proteins fold?” at the TR+ Conference 2026: From Static to Dynamics, organized by LINXS and MAX IV in Lund from 21 to 23 September 2026. The conference brings together researchers working on time-resolved methods to investigate the structural and functional dynamics of biomolecules.

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 six decades ago, this code remains undefined. Although 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.

Reference: Bustamante, C., Kaiser, C. M., Lindahl, E., Sosa, R., & Volpe, G. (2026). “How proteins fold.” Nature Reviews Molecular Cell Biology. Advance online publication.

Seminar by S. K. Manikandan at the Department of Applied Physics, University of Tokyo, 8 September 2026

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.)
Nonequilibrium Fluctuations as Probes of Thermodynamics
Date: 8 September 2026
Time: 10:30
Place: Department of Applied Physics, University of Tokyo

The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.

Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for addressing these challenges. In this talk, I will first introduce recent theoretical results showing how nonequilibrium fluctuations can be exploited to quantify energy dissipation at scales inaccessible to conventional calorimetry. I will then demonstrate the practical applicability of these results by quantifying energy dissipation from experimental data in real physical systems, as well as estimating free-energy differences from nonequilibrium measurements in highly dissipative regimes where conventional fluctuation-theorem-based methods fail. I will conclude by discussing the remaining challenges and open questions.

Seminar by H. Rubinsztein-Dunlop, Gothenburg, 30 September 2026

Halina Rubinsztein-Dunlop. (Photo by A. Ciarlo.)
Quantum sensing and mechanobiology with optically trapped nanodiamonds
Professor Halina Rubinsztein-Dunlop
Date: 30 September 2026
Time: 12:30-13:30
Place: PJ Salen
Registration: register here

Mechanobiology can be studied using optical micromanipulation enabling determination of forces, displacements and torques acting in complex out of equilibrium biological systems. Optical tweezers have been used for such studies very successfully. Transfer of optical angular momentum, (OAM), to matter broadens these applications to the rotational domain. The OAM is used for introducing rotation and torque to the system enabling application such as nano- and microviscoelasticity and production of all optically driven nano and micromachines.
Nanodiamonds (NDs) containing nitrogen- vacancy (NV) centres have raised interest as multifunctional probes for their unique possibility of being fluorescent scanning probes, combining spin optical read-out that makes them unique quantum sensors. Combining optical micromanipulation and NV centres in nanodiamonds (NDs) focuses on the advantages and achievements in controlling the NDs positioning that enables precise magnetic sensing and measurements of other highly relevant parameters of the environment that they are in such as minute temperature changes and PH. The measurements combine force, position and torque with fluorescence detection and optically detected magnetic resonance, ODMR. For high precision measurements of this type it is essential to be able to control precisely the position of the nanodiamonds. We are developing methods that combine rotational optical tweezers and quantum sensing with FNDs to achieve this goal.

Short bio:
Prof. Halina Rubinsztein-Dunlop, is Deputy Director of the ARC CoE in Quantum Biotechnology and runs Translational Research Portfolio there. She is Professor of Quantum Physics, at University of Queensland. At UQ, she leads groups in experimental quantum atom optics, laser micromanipulation and biophotonics. Halina is the recipient of many national and international awards, including Officer in the General Division of the Order of Australia. She is a Fellow of Australian Academy of Science, a Fellow of SPIE, Optica and AIP. In 2024 Halina was awarded 2024 SPIE Directors’ Medal. In the same year she was also awarded an Honorary Doctor of Science honoris causa by the University of Glasgow. In January 2025 Halina was named as a recipient of SPIE Gold Medal 2025. Halina is actively pursuing work in the Equity, Diversity and Inclusion area and is involved in popularisation and promotion of science.

Invited talk by S. K. Manikandan at the Yukawa Institute of Theoretical Physics, Kyoto, 2 September 2026

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.)
Nonequilibrium Fluctuations as Probes of Thermodynamics
Date: 2 September 2026
Time: 16:00
Place: Seminar Room K202, Main Building, Yukawa Institute, Kyoto U.

The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.

Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for addressing these challenges. In this talk, I will first introduce recent theoretical results showing how nonequilibrium fluctuations can be exploited to quantify energy dissipation at scales inaccessible to conventional calorimetry. I will then demonstrate the practical applicability of these results by quantifying energy dissipation from experimental data in real physical systems, as well as estimating free-energy differences from nonequilibrium measurements in highly dissipative regimes where conventional fluctuation-theorem-based methods fail. I will conclude by discussing the remaining challenges and open questions.

Presentation by A. Domenzain at SPIE-ETAI, San Diego, 25 August 2026

Particle tracking turns microscopy videos into measurable motion, revealing how cells, molecules, and materials move and interact. Combining standard and deep learning-based methods, users can create custom, modular pipelines enabling high-throughput, automated and unbiased trajectory determination.  (Image by A. Domenzain)
Particle tracking in quantitative microscopy: a modular tutorial on classical and deep learning methods
Aarón Domenzain, Alex Lech, Jesco Schönfelder, Marta Conti, Daniel Midtvedt, Marcel Rey, Carlo Manzo, Giovanni Volpe
Date: 25 August 2026
Time: 9:35 AM – 9:50 AM PDT
Place: Conv. Ctr. Room 2

Particle tracking is a central tool in digital microscopy for quantifying micro- and nanoscale dynamics. Selecting and validating appropriate methods remains challenging, particularly under low signal-to-noise ratios or high particle densities and for researchers with limited computational expertise. In this talk, we present a modular, Python notebook-based tutorial that organizes tracking into two stages—object detection and trajectory linking—providing a structured basis for systematic comparison of classical and deep learning approaches and stage-specific performance diagnosis. For detection, we compare intensity thresholding and Gaussian feature localization (Crocker-Grier) with convolutional neural networks, including supervised segmentation (U-Net) and a self- supervised geometric deep learning model (LodeSTAR). For linking, we evaluate nearest-neighbor and Hungarian assignment methods alongside a graph neural network classifier (MAGIK). Hands-on examples using simulated datasets with ground truth support quantitative benchmarking that identifies performance regimes and computational trade-offs. The resulting workflows are ready-to-use and adaptable across diverse scenarios.

Invited presentation by G. Volpe at SPIE-OTOM, San Diego, 25 August 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.)
Artificial intelligence for optical tweezers (Invited Paper)
Giovanni Volpe
Date: 25 August 2026
Time: 9:30 AM – 10:00 AM PDT
Place: Conv. Ctr. Room 5B

Artificial intelligence (AI) is rapidly transforming optical tweezers from manually tuned instruments into adaptive, self-optimizing experimental platforms. By integrating real-time image analysis, physics-informed inference, and feedback control, AI can dynamically estimate particle states, environmental fluctuations, and trap properties, enabling continuous optimization of trapping conditions. This approach enhances stability, precision, and robustness in complex or nonstationary environments while reducing operator intervention. As a representative application, we present SmartTrap, an AI-driven control framework that autonomously adjusts trap parameters in real time to improve manipulation performance. AI-enabled optical tweezers open new opportunities for adaptive force spectroscopy, intelligent probing of active systems, and the development of autonomous experimental workflows in soft matter physics and biophysics.

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]

Presentation by A. Patil at SPIE-ETAI, San Diego, 24 August 2026

Beyond pixels: deep feature extraction for plant nutrient deficiency classification
Anoop C. Patil, Ji-Yan Wu, Shalini Krishnamoorthi, Gajendra P. Singh, BongSoo Park, Daisuke Urano, Giovanni Volpe
Date: 24 August 2026
Time: 2:00 PM – 2:15 PM PDT
Place: Conv. Ctr. Room 2

Accurate detection of nutrient deficiencies from leaf images is critical for early nutrient stress identification and timely corrective intervention. We investigate controlled nutrient treatments to plant leaves representing complete absence (0%) and partial presence (5%) of iron, nitrogen, and phosphorus, alongside a healthy condition. The notation 0_X denotes complete absence of nutrient X (e.g., 0_Fe), while 5_X denotes 5% availability (e.g., 5_Fe) in plant leaves. We propose a two-stage framework in which a pretrained EfficientNet-B0 model extracts compact visual features from cropped leaf images,and lightweight machine-learning classifiers predict nutrient classes. Compared to raw RGB baselines,the deep-feature approach achieves higher classification accuracy, enabling robust identification of graded nutrient deficiencies for quantitative plant stress phenotyping.