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.
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.
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.
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.
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.
A centralized, peer-reviewed ecosystem in J. Phys. Photonics where human agents and/or AI agents co-develop validated, documented software. (Image from the manuscript.)Beyond the black box: towards an open and citable software ecosystem in photonics
Anoop C Patil, Maciej Trusiak, Fei Xia, Liangcai Cao, Carlo Manzo and Giovanni Volpe
Journal of Physics: Photonics 8, 020201 (2026)
doi: 10.1088/2515-7647/ae68cd
This editorial draws attention to a major issue in photonics: important research software is often hidden, undocumented, and lost over time, making results hard to reproduce. The rise of AI-generated code increases this problem by adding more ‘black box’ systems. To address this, J. Phys. Photonics is introducing Software Articles, a new article type allowing researchers to publish, validate, and share their code as formal scientific outputs. This initiative aims to promote transparency, reproducibility, and proper credit for developers. Open and peer-reviewed software helps the community verify results, reduce duplication of effort, and build lasting tools, ensuring that computational methods become reliable, accessible, and integral to scientific progress.
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
Physical Review Research 8, L022017 (2026)
arXiv: 2505.11042
doi: 10.1103/xn9x-ppjx
Active systems in nature and synthetic environments commonly exhibit spatially heterogeneous activity patterns and time-delayed responses from internal feedback mechanisms, exemplified by bacterial chemotaxis. We study an idealized active gas where particles modulate their self-propulsion based on local environmental conditions with such delays. Through integrated theoretical, computational, and experimental approaches, we demonstrate that steady-state density distributions and collective polarization exhibit characteristic peaks and valleys as functions of response delay time. We find that delays can amplify polarization by nearly an order of magnitude and trigger complete polarization reversal when particle displacement during the delay period surpasses the persistence length. Multiparticle simulations incorporating interparticle interactions validate that these phenomena remain robust in sufficiently dilute collective systems. Since density and polarization determine the current in active matter, our findings show that temporally programming the delay time allows control over both static and dynamic states in active systems, with implications for biological microswimmers and engineered microrobots.
Visualization of the vasculature within human bone from a 75-year-old patient by immunostaining with antibodies against CD31. (Image from the manuscript.)Three-dimensional quantitative tissue clearing reveals differences in osteovascular niche of aged and young human mesenchymal stromal cells
Nelson Tsz Long Chu, Ostap Dregval, Yu-Wei Chang, Emil Kriukov, Xin Tian, Xin Liu, Dana Trompet, Misty Shuo Zhang, Lei Li, Zhong Li, Emiliano Gomez Ruiz, Joana B. Pereira, Mats Brittberg, Björn Barenius, Lars Sävendahl, Ralf H. Adams, Inger Gjertsson, Claes Ohlsson, Giovanni Volpe & Andrei S. Chagin
Nature Biomedical Engineering (2026)
bioRxiv: 10.1101/2025.10.07.680053
doi: 10.1038/s41551-026-01645-3
Human bone marrow mesenchymal stromal/stem cells (BM-MSCs) are widely used in clinical trials and tissue engineering, yet their native microenvironment remains poorly understood. Here we introduce a tissue-clearing protocol, DeepBone, for human bones and integrate it with simultaneous mRNA and protein detection. Using this protocol, we spatially map BM-MSCs relative to key bone microenvironment components, including human blood capillaries, adipocytes, sinusoids and bony trabeculae. Quantitative analysis reveals that the native microenvironment of human BM-MSCs in young bone is enriched in vasculature, sinusoids, bone matrix and adipocytes. In contrast, in aged bone, BM-MSCs show no preferential association with bone or adipocytes. Proliferative BM-MSCs are predominantly found along blood vessels. Moreover, we identify a specialized microenvironment for BM-MSCs in young bone, characterized by sinusoids coiled around trabeculae and enriched by R-type vessels. These findings provide insights into the native niches of BM-MSCs, offering a foundation for the development of tissue engineering strategies that mimic their physiological context.
Automated segnmentation of bacterial structures within a droplet. The image shows a bright-field microscopy view where a large biofilm region (green, outlined in blue) has been segmented from surrounding features. Small aggregates (yellow contours) are also highlighted. This segmentation enables structural differentiation of biofilm components for downstream quantitative analysis. (Image by D. Pérez Guerrero.)Latent space-driven quantification of biofilm formation using time-resolved droplet microfluidics
Daniela Pérez Guerrero, Jesús Manuel Antúnez Domínguez, Aurélie Vigne, Daniel Midtvedt, Wylie Ahmed, Lisa D. Muiznieks, Giovanni Volpe, Caroline Beck Adiels
Microchemical Journal 225, 117685 (2026)
arXiv: 2507.07632
DOI: 10.1016/j.microc.2026.117685
Bacterial biofilms play crucial roles across diverse contexts, from public health risks to beneficial applications in bioremediation, biodegradation, and wastewater treatment. However, tools that enable high-resolution, dynamic analysis of their responses to environmental cues and collective cellular behaviors remain limited. Here, we present a droplet-based microfluidic platform that combines continuous in situ microscopy with subsequent unsupervised deep learning for quantitative analysis of biofilm development. In our setup, Bacillus subtilis cells are encapsulated in monodisperse aqueous microdroplets containing Lysogeny Broth, suspended in an oil phase and immobilized within microfabricated traps, providing continuous optical access throughout biofilm formation at the water–oil interface. The platform supports both fluorescence and bright-field imaging, enabling high-throughput, time-resolved monitoring of thousands of droplets under controlled conditions. To extract quantitative information from these large datasets, we developed an automated analysis pipeline based on a Variational Autoencoder (VAE) trained directly on microscopy images from our experiments. This unsupervised model enables segmentation and latent-space representation of bacterial structures without manual annotation or synthetic training data. Post-segmentation size thresholding enables classification of bacterial aggregates and larger biofilm-like clusters, including quantification of biofilm porosity, thereby supporting detailed morphological and temporal analyses across droplets and conditions. By integrating droplet microfluidics with unsupervised deep learning, our platform provides a scalable, robust, and rapid approach for high-throughput quantitative studies of biofilm behavior. It resolves complex structural biofilm patterns, bypasses the need for manual annotation, and opens new opportunities to probe environmental determinants of biofilm formation. Departing from earlier methods, our framework fuses biological training data with unsupervised models to quantify microbial community dynamics across scales, offering a generalizable platform for future high-resolution microbiology.
Diagnostic classification. (Image from the article.)Mapping individual molecular connectomes in Alzheimer’s disease
Zhilei Xu, Mite Mijalkov, Jiawei Sun, Yu-Wei Chang, Arianna Sala, Giovanni Volpe, Mario Severino, Mattia Veronese, Sara Garcia-Ptacek, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71310 (2026)
DOI: 10.1002/alz.71310
INTRODUCTION
Mapping individual differences is crucial to improve personalized medicine approaches in Alzheimer’s disease (AD), which is characterized by strong inter-individual variability in the accumulation patterns of tau and amyloid beta pathology.
METHODS
We assess the progression of AD across the disease continuum by building individual molecular connectomes using longitudinal positron emission tomography (PET) data.
RESULTS
We demonstrate that these connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects. Alterations in the connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional PET measures. We introduce a novel gene-specific transcription network analysis that linked individual tau and amyloid connectomes to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.
DISCUSSION
Individual molecular connectome mapping provides a novel and sensitive framework to monitor AD progression.
Highlights
Individual molecular connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects.
Alterations in individual molecular connectomes significantly increase both across the Alzheimer’s disease (AD) continuum and over time.
Alterations in individual molecular connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional positron emission tomography measures.
Susceptibilities of individual tau and amyloid connectomes to AD are linked to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.
