arXiv subjects

Giancarlo Ruocco

Publications and source records attributed to Giancarlo Ruocco.

At least 19 recordsLinked to original sources

arXiv2026

On non-phononic modes and glassy dynamics

The relationship between vibrational modes and glassy dynamics has been an important and controversial topic of study for many decades. We introduce a family of glass-forming model potentials that have identical molecular and swap dynamics but different low-frequency vibrational modes. In sufficiently small systems low frequency modes do not hybridize with phonons, and this enables us to study their statistical properties independently. We investigate i) the role of the optimization algorithm used to find a minimum; ii) that of the finite-size effects; iii) that of the truncation of the interaction potential; and, iv) that of the parent temperature. We find that models with identical supercooled liquid structure and dynamics can have dramatically different non-phononic vibrational modes at low frequency, based on how the potential is truncated. These findings challenge ideas relating the low-frequency non-phononic vibrational modes with the slow supercooled liquid relaxation.

cond-mat.dis-nn↗

arXiv2026

How genome redundancy can promote evolutionary innovation

Polyploidy is defined as the existence of more than two complete sets of homologous chromosomes. Despite it being a widespread phenomenon across the tree of life, its role as either an evolutionary innovation or a dead end is still debated. Here, we investigate how under varying selective pressures the degree of ploidy interacts with two key biological factors: the mode of inheritance and the genotype-phenotype mapping. Through a minimal evolutionary model we find that polyploidy is especially advantageous during abrupt environmental changes, confirming that polyploidization is often associated with ecological upheavals. We observe that stochastic inheritance combined with a nonlinear (maximum-based) genotype-phenotype mapping maximizes both phenotypic exploitation and landscape exploration across all environments. By contrast, structured inheritance with an additive phenotype mapping systematically underperforms, yet displays a pronounced optimum at low-to-intermediate ploidy level that mirrors the distribution observed in natural plant and bacterial populations. When individuals are free to carry different chromosome numbers, selection drives the population toward values that reflect an interplay between exploitation, exploration, and convergence speed rather than any single evolutionary objective. The relative weight of these three factors depends on the fitness landscape, providing a unifying framework for understanding when and why polyploidy is favored by natural selection.

q-bio.PE↗

arXiv2026

Classical versus quantum Anderson localization in disordered systems

We investigate Anderson localization in three-dimensional disordered systems by comparing scalar classical waves with mass and force-constant disorder to electronic tight-binding models with diagonal and off-diagonal disorder. We show that the commonly employed mapping between classical-wave localization and the electronic Anderson model with diagonal disorder is not mathematically justified. Instead, the correct modulus-type formulation reveals that classical-wave systems constitute a distinct constrained disorder class, in which the acoustic sum rule correlates diagonal and off-diagonal matrix elements and prevents any direct correspondence with the standard electronic disorder models. Within a unified eigenvalue framework, we determine localization phase diagrams for all four disorder classes using complementary spectral, eigenvector, and level-statistics diagnostics. We find that classical-wave systems share a key qualitative feature with electronic off-diagonal disorder: localized states occur only near a band edge, while extended states persist in the central part of the spectrum even at strong disorder. At the same time, the acoustic sum rule produces localization topologies that differ fundamentally from both diagonal- and off-diagonal-disorder electronic systems. In particular, for mass disorder we obtain a phase diagram that differs qualitatively from previous results based on the conventional potential-type approach and reveals an extended localized regime near the upper band edge. Our results establish a unified perspective on localization in quantum and classical wave systems and provide new insight into the conditions under which Anderson localization may occur in three-dimensional photonic and acoustic media.

cond-mat.dis-nn↗

arXiv2026

Observation of associative-memory retrieval and spin-glass phases on a photonic quantum simulator

Models of interacting complex systems provide the fundamental statistical physics reference frame for the study and the understanding of associative memories, machine learning, and the dynamics of neural networks. On the other hand, simulating complex multi-synaptic interactions on a classical hardware is computationally demanding due to the super-linear scaling of the system complexity. Photonic quantum technologies provide a promising solution to these limitations by leveraging on their inherent speed and parallel processing ability in order to simulate complex networks. Recently, a connection between multiphoton processes and generalized $p$-body Hopfield models has been theoretically established. Here, we design and demonstrate an experimental platform that exploits single photons distributed across a set of optical modes, in which controlled arrays of binary phase shifters act as Ising-like neurons. We focus specifically on a fully connected Hopfield Hamiltonian with four-body local interaction terms, realized via two-photon processes. Through quantum simulations on programmable photonic processors, the study identifies three distinct regimes: a memory retrieval phase, a spin-glass memory "black-out" phase, and a paramagnetic phase. Experimental results confirm successful memory retrieval at low storage capacities and temperatures, where the system consistently relaxes to fixed points with high memory overlap, effectively reconstructing the stored patterns. Future research will extend the platform design to investigate networks with local or dilute interactions, while advances in the realization of scalable photonic circuits will enable architectures that encompass very large numbers of interacting spins.

quant-ph↗

arXiv2026

Non-equilibrium phase transitions in hybrid Voronoi models of cell colonies

Eukaryotic cells are characterized by a stiff nucleus whose effect in modeling the collective behavior of cell aggregates is usually underestimated. However, increasing experimental evidence links nuclear modifications with phenotypic transition, like the one between epithelial and mesenchymal states. In this work, we explore the effect of short-range repulsive forces in the non-equilibrium dynamics of the self-propelled Voronoi model. We show that the competition between steric repulsions (representing nuclear/cellular compressibility) and Vertex interactions (mimicking cell-cell adhesion/interaction and cytoskeleton organization) generate a variety of non-equilibrium phase transitions from Motility-Induced Phase Separation to mesenchymal-like phases up to disordered confluent configurations. Notably, we found that tuning the nucleus's effective size/compressibility provides an additional way to cross the boundary between the different possible phases in line with experimental observations.

cond-mat.soft↗

arXiv2026

Further testing the validity of generalized heterogeneous-elasticity theory for low-frequency excitations in structural glasses

We summarize the salient features of our theory of non-phononic vibrational excitations in glasses [W. Schirmacher et al., Nature Comm. 15, 3107 (2024)]. Next, we provide further evidence of the non-universality of the $ω^4$ scaling of the non-phononic vibrational density of states (DoS), and the existence of an important class of non-phononic excitations in glasses, which we call defect states. These modes are induced by frozen-in stresses and can be classified as quasi-localized. Our results suggest that the commonly observed low-frequency $ω^4$ scaling of the non-phononic vibrational density of states is highly dependent on technical aspects of the molecular dynamics simulations employed to compute the DoS.

cond-mat.dis-nn↗

arXiv2026

Deciphering the chemical grammar of protein-RNA condensates

Biomolecular phase separation is typically attributed to the polymer physics of long, disordered chains. However, the underlying chemical grammar, i.e. the specific interactions between protein and RNA building blocks, remains poorly understood. We decouple those effects by screening the phase behavior of the complete dipeptide library in presence and absence of nucleic acids using full-atomistic molecular dynamics simulations. We demonstrate that (i) even these ultrashort units encode the instructions for spontaneous condensation, proving that phase separation is fundamentally rooted at a sub-polymeric level. (ii) Nucleic acids do not act as generic anionic glue but exert instead a base-specific regulatory logic. (iii) Individual nucleobases function as chemical tuners that dissolve, stabilize, or fluidize condensates based on their molecular identity. Overall, our minimal framework reveals that while polymer length enhances assembly, the core properties and regulatory control of condensates may be also governed by a fine-tuned chemical alphabet of peptides and nucleobases.

physics.bio-ph↗

arXiv2026

Electro-Optic Modulator source as sample-free calibrator and frequency stabilizer for Brillouin Microscopy

Brillouin Microscopy is a novel label-free optical technique that enables the measurement of a material's mechanical properties at the sub-micron scale in a non-invasive and non-contact way; in the last few years, its applications in the life sciences have extensively expanded. To date, many custom-built Brillouin Microscopes suffer from temporal instabilities that impact their performances, showing drifts in the acquired spectra during time that may lead to inconsistencies between data acquired at different days. A further challenge for standard Brillouin Microscopes is the calibration of the spectrometer: the currently accepted protocol in literature uses known Brillouin shifts of water and methanol to reconstruct the dispersion curve, but this approach is highly influenced by external factors that are unrelated to spectrometer's performances. Manual and frequent realignments of the spectrometer and repeated calibrations with standard materials are thus needed to address these issues. Here, we show an innovative method to remove temporal instabilities of a standard Brillouin Microscope by inserting an Electro-Optic Modulator (EOM) that can be used: i) as a reference signal during measurements; ii) as a calibrator, allowing the reconstruction of the spectrometer dispersion curve with high precision, in an automatic pipeline and without the need for reference samples; iii) as a tool to detect and compensate for temporal drifts through a feedback control in a closed loop. We here show that our Brillouin Microscope, equipped with an EOM and a tuneable laser, is able to automatically acquire data for more than 2 days without the need to realign the spectrometer; retrieved Brillouin shifts and widths showed superior stability in time than standard Brillouin Microscopes.

physics.bio-ph↗

arXiv2025

Emergent learning: neuromorphic photonic computing with accelerated training

Emergent learning transforms a disordered optical medium into a photonic device capable of storage, recognition, and classification of arbitrary memory patterns. First, we show that the intensity at the output of a multiply scattering system can be described by a dyadic matrix, the optical-synaptic matrix, exhibiting the same form as a Hebbian synaptic matrix containing a single memory. Then, we employ emergent learning - an approach inspired by neuroscience - to exploit the vast dictionary of raw memories inherently available within a disordered optical structure, thereby engineering the optical-synaptic matrix to store a user-defined attractor, or tailored memory. Importantly these photonic structures also works as an optical comparators providing an intensity-based measure of the degree of similitude between a query pattern and the stored pattern, realizing an hardware co-localization between memory and optical operator. Our system has an almost infinite hardware capacity of tailored memories/ operators ($\mathcal{M} \sim 10^{60557}$), thus these tailored memories can be then employed as examples to build a classifier hardware based on intensity comparison without the need of additional digital transformation layers. Remarkably, this Photonic Emergent Learning platform is not only flexible and fabrication-free, but also relies primarily on analog processes, thus shifting the computational burden of training from the digital layers to the optical domain reducing the computational cost and enhancing performance.

physics.optics↗

arXiv2025

Multiphoton quantum simulation of the generalized Hopfield memory model

In the present work, we introduce, develop, and investigate a connection between multiphoton quantum interference, a core element of emerging photonic quantum technologies, and Hopfieldlike Hamiltonians of classical neural networks, the paradigmatic models for associative memory and machine learning in systems of artificial intelligence. Specifically, we show that combining a system composed of Nph indistinguishable photons in superposition over M field modes, a controlled array of M binary phase-shifters, and a linear-optical interferometer, yields output photon statistics described by means of a p-body Hopfield Hamiltonian of M Ising-like neurons +-1, with p = 2Nph. We investigate in detail the generalized 4-body Hopfield model obtained through this procedure and show that it realizes a transition from a memory retrieval to a memory black-out regime, i.e. a spin-glass phase, as the amount of stored memory increases. The mapping enables novel routes to the realization and investigation of disordered and complex classical systems via efficient photonic quantum simulators, as well as the description of aspects of structured photonic systems in terms of classical spin Hamiltonians.

quant-ph↗

arXiv2025

Robust assessment of asymmetric division in colon cancer cells

Asymmetric partition of fate determinants during cell division is a hallmark of cell differentiation. Recent work suggested that such a mechanism is hijacked by cancer cells to increase both their phenotypic heterogeneity and plasticity and in turn their fitness. To quantify fluctuations in the partitioning of cellular elements, imaging-based approaches are used, whose accuracy is limited by the difficulty of detecting cell divisions. Our work addresses this gap proposing a general method based on high-throughput flow cytometry measurements coupled with a theoretical framework. We applied our method to a panel of both normal and cancerous human colon cells, showing that different kinds of colon adenocarcinoma cells display very distinct extents of fluctuations in their cytoplasm partition, explained by an asymmetric division of their size. To test the accuracy of our population-level protocol, we directly measure the inherited fractions of cellular elements from extensive time-lapses of live-cell laser scanning microscopy, finding excellent agreement across the cell types. Ultimately, our flow cytometry-based method promises to be accurate and easily applicable to a wide range of biological systems where the quantification of partition fluctuations would help accounting for the observed phenotypic heterogeneity and plasticity

q-bio.CB↗

arXiv2025

A mother-machine microfluidic device for non-adherent mammalian cells reveals the population growth strategies

We develop a mother machine-like microfluidic device specifically designed to track the proliferation of T-cells via live-cell microscopy. Although numerous microfluidic setups have been developed to study cell proliferation at the single-cell level, most of them are optimized for use on adherent cells. Here, we present a device to track the proliferation of suspension cells, featuring an array of microchannels that trap cells, easing their monitoring while allowing for controlled growth conditions. Each microchannel, whose geometry has been optimized through computational fluid dynamics simulations, allows a single cell to enter and proliferate while maintaining a continuous flow of nutrients, ensuring long-term monitoring over multiple generations. We show the advantages of this system in characterizing the proliferation of human leukemia T-cells. In particular, we follow the growth and division over multiple generations, finding that cells exhibit a slightly asymmetric volume division where deviations in the size are compensated by a size-like division strategy. Overall, our device design can be easily adapted and used to study different cell types and sizes while maintaining the same high trapping efficiency.

q-bio.CB↗

arXiv2025

Internal Stresses as Origin of the Anomalous Low-Temperature Specific Heat in Glasses

We apply a recently developed theory of the nonphononic vibrational density of states (DOS) in glasses to investigate the impact of local frozen-in stresses on the low-temperature specific heat. Using a completely harmonic description we show that the hybridization of the local nonphononic vibrational excitations with the waves leads to a low-frequency DOS, in excess to the Debye one, which varies linearly with frequency up to a certain crossover frequency, and then becomes constant. The actual value of the crossover depends of the ratio between the local stresses and the shear modulus. This excess DOS leads to a low-temperature specific heat with an apparent temperature exponent, which is between one and two, as observed experimentally. We discuss, how these findings may be utilized for the characterisation of glassy materials. We further compare our findings, which only rely on harmonic interactions, with the predictions of other theories, which invoke anharmonic interactions and tunneling for explaining the low-temperature behavior of the specific heat.

cond-mat.dis-nn↗

arXiv2025

Exotic collective dynamics in molten Carbon

Collective longitudinal and transverse propagating modes in molten Carbon at $T=5500$~K and pressure range $10$-$40$ GPa are reported from {\it ab initio} based as well as machine learned molecular dynamics. A striking exotic feature in collective dynamics is the two-peak shape of the current spectral functions of the single-component liquid, which makes evidence of a second branch of longitudinal propagating modes in the wave number range $k>1$Å$^{-1}$. It is shown that time correlation functions reflecting the out-of-phase motion of particles and their cages of nearest neighbors results in the same frequencies of the exotic low-frequency branch of vibrations. A theoretical framework of generalized collective modes is applied to recover the time dependence of density-density, imaginary part of susceptibility and longitudinal current-current correlations.

cond-mat.stat-mech↗

arXiv2025

Rosette formations as symmetry-breaking events: theory and experiment

Multicellular rosettes are observed in different situations such as morphogenesis, wound healing, and cancer progression. While some molecular insights have been gained to explain the presence of these assemblies of five or more cells around a common center, what are the tunable, global features that favors/hinders their formation is still largely unknown. Here, we made use of a Voronoi dynamical model to investigate the ingredients driving the emergence of rosettes characterized by different degree of stability and organization. We found that (i) breaking the local spatial symmetry of the system, i.e., introducing curvature-inducing defects, allows for the formation of rosette-like structures (ii) whose probability of formation depends on the characteristic of the cellular layer. In particular, a trade-off between tissue fluidity and single cell deformability dictates the assembly of transient rosettes, that are strongly stabilized in the presence of cell alignment interactions. To test our model predictions, we performed fluorescence microscopy experiments on rosette-forming neural populations derived from induced pluripotent stem cells, finding significant agreement. Overall, our work may set the stage to gain an unifying understanding of the plethora of biophysical mechanisms involving the occurrence of rosette-like structure both in physiology and their altered formation in pathology.

q-bio.CB↗

arXiv2025

Translation-based structured illumination microscopy via generalized Richardson-Lucy method

Structured illumination microscopy (SIM) can achieve a $2\times$ resolution enhancement beyond the classical diffraction limit by employing illumination translations with respect to the object. This method has also been successfully implemented in a ``blind'' configuration, i.e., with unknown illumination patterns, allowing for more relaxed constraints on the control of illumination delivery. Here, we present a similar approach using a novel super-resolution algorithm that employs a generalized version of the popular Richardson-Lucy algorithm, alongside an optimized and customized optical setup. Both numerical and experimental validations demonstrate that our technique exhibits high noise resilience. Moreover, by implementing random translations instead of ``ordered'' ones, noise-related artifacts are reduced. These advancements enable wide-field super-resolved imaging with significantly reduced optical complexity.

physics.optics↗

arXiv2025

Insights into the role of dynamical features in protein complex formation: the case of SARS-CoV-2 spike binding with ACE2

The functionality of protein-protein complexes is closely tied to the strength of their interactions, making the evaluation of binding affinity a central focus in structural biology. However, the molecular determinants underlying binding affinity are still not fully understood. In particular, the entropic contributions, especially those arising from conformational dynamics, remain poorly characterized. In this study, we explore the relationship between protein motion and binding stability and its role in protein function. To gain deeper insight into how protein complexes modulate their stability, we investigated a model system with a well-characterized and fast evolutionary history: a set of SARS-CoV-2 spike protein variants bound to the human ACE2 receptor, for which experimental binding affinity data are available. Through Molecular Dynamics simulations, we analyzed both structural and dynamical differences between the unbound (apo) and bound (holo) forms of the spike protein across several variants of concern. Our findings indicate that a more stable binding is associated with proteins that exhibit higher rigidity in their unbound state and display dynamical patterns similar to that observed after binding to ACE2. The increase of binding stability is not the sole driving force of SARS-CoV-2 evolution. More recent variants are characterized by a more dynamical behavior that determines a less efficient viral entry but could optimize other traits, such as antibody escape. These results suggest that to fully understand the strength of the binding between two proteins, the stability of the two isolated partners should be investigated.

q-bio.BM↗

arXiv2025

Evidence of Scaling Regimes in the Hopfield Dynamics of Whole Brain Model

It is shown that a Hopfield recurrent neural network exhibits a scaling regime, whose specific exponents depend on the number of parcels used and the decay length of the coupling strength. This scaling regime recovers the picture introduced by Deco et al., according to which the process of information transfer within the human brain shows spatially correlated patterns qualitatively similar to those displayed by turbulent flows, although with a more singular exponent, 1/2 instead of 2/3. Both models employ a coupling strength which decays exponentially with the Euclidean distance between the nodes, informed by experimentally derived brain topology. Nevertheless, their mathematical nature is very different, Hopf oscillators versus a Hopfield neural network, respectively. Hence, their convergence for the same data parameters, suggests an intriguing robustness of the scaling picture.Furthermore, the present analysis shows that the Hopfield model brain remains functional by removing links above about five decay lengths, corresponding to about one sixth of the size of the global brain. This suggests that, in terms of connectivity decay length, the Hopfield brain functions in a sort of intermediate ``turbulent liquid''-like state, whose essential connections are the intermediate ones between the connectivity decay length and the global brain size. The evident sensitivity of the scaling exponent to the value of the decay length, as well as to the number of brain parcels employed, leads us to take with great caution any quantitative assessment regarding the specific nature of the scaling regime.

cond-mat.dis-nn↗