Author: Agnese Callegari

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

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.

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.

Presentation by A. Ciarlo at SPIE-OTOM, San Diego, 23 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.)
SmartTrap: high-throughput optical tweezers via real-time adaptive control
Antonio Ciarlo, Martin Selin, 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
Date: 23 August 2026
Time: 4:55 PM – 5:10 PM PDT
Place: Conv. Ctr. Room 5B

We present SmartTrap, a fully autonomous optical tweezers platform that automates optical trapping experiments. SmartTrap uses deep learning–based 3D particle tracking, adaptive feedback control, and automated microfluidics to perform complete force spectroscopy and manipulation protocols without human intervention. Once initialized, the system operates continuously, autonomously handling trapping, molecular attachment, force application, data acquisition, and bead replacement. We demonstrate its capabilities using automated λ-DNA pulling experiments, which enable high-throughput force-extension and kinetic measurements. These capabilities are implemented using fully open-source control and analysis software with user-friendly interfaces, facilitating adaptation to existing optical tweezers setups and experimental workflows. SmartTrap is a flexible, general-purpose platform for automated optical trapping experiments in biomolecular, colloidal, and cellular systems.