Category: Presentation

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

Quantum sensing and mechanobiology with optically trapped nanodiamonds
Professor Halina Rubinsztein-Dunlop
Date: 30 September 2026
Time: 12:30-13:30
Place: PJ Salen

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.

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

From Raman to infrared: extending DIVA to FTIR spectra for quantitative seed phenotyping
Anoop C. Patil, Sreelatha Sarangapani, Yu-Wei Chang, Kasi Viswanath Kotapati, Ganga Sravanthi Cheerlavancha, Joana B. Pereira, Raju Cheerlavancha, Mervin Chun-Yi Ang, Gajendra P. Singh, Rajani Sarojam, Giovanni Volpe
Date: 24 August 2026
Time: 1:45 PM – 2:00 PM PDT
Place: Conv. Ctr. Room 2

Vibrational spectroscopy enables non-destructive probing of biomolecular composition in biological systems. We extend our Deep-learning Investigation of Vibrational spectra (DIVA) framework, originally developed for Raman spectral analysis, to Fourier-transform infrared (FTIR) spectroscopy for seed characterization. The pipeline integrates absorbance normalization, first-derivative transformation, and variational autoencoder (VAE)–based latent representation learning to capture structured spectral variability across wildtype and treated seed categories. Consensus derivative bands and pairwise spectral differences are extracted to identify discriminative biomolecular features between treated and wild-type seeds. Latent-space embeddings enable visualization of cluster separability and support classification. This work demonstrates that DIVA provides a scalable framework for quantitative FTIR analysis, enabling robust unbiased assessment of seed condition.

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

DeepTrack2 Logo. (Image by J. Pineda)
DeepTrack2: Microscopy Simulations for Deep Learning
Alex Lech, Mirja Granfors, Jiacheng Huang, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, Giovanni Volpe
Date: 24 August 2026
Time: 11:45 AM – 12:00 PM PDT
Place: Conv. Ctr. Room 2

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. Simulations can be executed using a PyTorch backend, enabling GPU acceleration and backpropagation through the optical system, and allowing seamless integration with deep learning workflows. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Invited Presentation by P. Dutta at SPIE-ETAI, San Diego, 24 August 2026

A coarse-grained molecular dynamics framework used to simulate plasmid DNA analyzed via atomic force microscopy (AFM). The resulting images are used to train a U-Net for DNA chain and crossing segmentation and classification. (Image by P. Dutta.)
ASAP (AFM simulation and analysis pipeline): a unified deep learning framework for simulation, quality control, and segmentation in atomic force microscopy (Invited Paper)
Prakhar Dutta, Jiacheng Huang, Nazli Demirpehlivan, Thomas Catley, Sylvia Whittle, Carlo Manzo, Rahul Nagshi, Rachel Owen, Giovanni Volpe
Date: 24 August 2026
Time: 10:30 AM – 11:00 AM PDT
Place: Conv. Ctr. Room 2

Atomic force microscopy (AFM) resolves biological structure and mechanics at high resolution, but produces heterogeneous datasets that are noisy and time-consuming to analyze. Although deep learning could automate quality control, segmentation and feature extraction, adoption is limited by scarce ground-truth training data and technical barriers for experimentalists. Here we present ASAP, an open-source tutorial and pipeline implemented in DeepTrack to provide a foundation for AI-enabled AFM. We demonstrate the framework with three examples: (i) an unsupervised variational autoencoder for force-curve quality control; (ii) a dual pathway simulation for DNA, offering both molecular dynamics and rapid, non-MD geometries to generate ground truth for segmentation training; and (iii) a versatile simulation framework that generates synthetic force curves for user-defined surfaces by applying selectable physical models. By consolidating simulation and learning into a modular ecosystem, this work enables users to utilize our pipeline to optimize AFM workflows for efficient data acquisition and processing.

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.