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Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long times required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral-scanning based hyperspectral imaging setup, named HyperProbe1, that can provide in situ, fast biochemical analysis and mapping of fresh surgical tissue samples, right after excision, and without the need of fixing or staining. HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tuneable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to 79), and to reconstruct 3D hypercubes composed of high-resolution, widefield images of the surgical samples, where each pixel is associated with a complete spectrum. The system is applied on 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumour classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct mapping of major biomarkers. We also provided a preliminary attempt to infer tumour classification based on differences of composition in the samples, suggesting the possibility to use lipid content and differential cytochrome-c-oxidase concentrations to distinguish between lower and higher grade gliomas. A proof-of-concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

Spontaneous brain activity in the absence of external stimuli is not random but contains complex dynamical structures such as neuronal avalanches with power-law duration and size distributions. These experimental observations have been interpreted as supporting evidence for the hypothesis that the brain is operating at criticality and attracted much attention. Here, we show that an entire state of coherent bursting, with power-law distributed avalanches and features as observed in experiments, emerges in networks of adaptive neurons with stochastic input when excitation is sufficiently strong and balanced by adaptation. We demonstrate that these power-law distributed avalanches are direct consequences of stochasticity and the oscillatory population firing rate arising from coherent bursting, which in turn is the result of the balance between excitation and adaptation, and criticality does not play a role.

The traditional degumming method using sodium carbonate solution severely damages the structure of silk fibroin and results in low molecular weight, which limits the properties and applications of silk materials. In this study, we report a modified degumming method and compared it with the traditional one. The results indicate that compared with the traditional degumming method, the modified degumming method produces silk fibroin with higher \b{eta}-sheet content (53.43% vs 46.68%), higher crystallinity (44.92% vs 42.72%), and more concentrated molecular weight (100 ~ 300 kDa vs 72 ~ 300 kDa), while allowing for the same full removal of sericin. Correspondingly, the film of the silk fibroin prepared with the modified degumming method exhibits higher \b{eta}-sheet content (35.22% vs 32.82%), higher crystallinity (26.96% vs 25.53%), and higher tensile strength (87.91 MPa vs 72.62 MPa). This work provides a degumming method superior to the traditional one and lays a foundation for the development of high-performance silk materials.

Competition during range expansions is of great interest from both practical and theoretical view points. Experimentally, range expansions are often studied in homogeneous Petri dishes, which lack spatial anisotropy that might be present in realistic populations. Here, we analyze a model of anisotropic growth, based on coupled Kardar-Parisi-Zhang and Fisher-Kolmogorov-Petrovsky-Piskunov equations that describe surface growth and lateral competition. Compared to a previous study of isotropic growth, anisotropy relaxes a constraint between parameters of the model. We completely characterize spatial patterns and invasion velocities in this generalized model. In particular, we find that strong anisotropy results in a distinct morphology of spatial invasion with a kink in the displaced strain ahead of the boundary between the strains. This morphology of the out-competed strain is similar to a shock wave and serves as a signature of anisotropic growth., Comment: 11 pages, 7 figures

Crossbridge binding and force in active muscle is dependent on the radial spacing between the thick and the thin filaments. This radial lattice spacing has been shown through spatially explicit modeling and experimental efforts to greatly affect isometric, force production in muscle. It has recently been suggested that this radial spacing might also be able to drive differences in mechanical function, or net work, under dynamic oscillations like those which occur in muscles in vivo. However, previous spatially explicit models either had no radial spacing dependence, meaning the lattice spacing could not be investigated, or did include radial spacing dependence but could not reproduce in vivo net work during dynamic oscillations and only investigated isometric contractions. Here we show the first spatially explicit model to include radial crossbridge dependence which can produce mechanical function similar to real muscle. Using this spatially explicit model of a half sarcomere, we show that when oscillated at strain amplitudes and frequencies like those in the hawk moth Manduca sexta, net work does depend on the lattice spacing. In addition, we can prescribe a trajectory of lattice spacing changes in the spatially explicit half sarcomere model and investigate the extent to which the time course of lattice spacing changes can affect mechanical function. We simulated a half sarcomere undergoing dynamic oscillations and prescribed the Poisson's ratio of the lattice to be either 0 (constant lattice spacing) or 0.5 (isovolumetric lattice spacing changes). We also simulated net work using lattice spacing data taken from Manduca sexta which has a variable Poisson's ratio. Our simulation indicates that the lattice spacing can change the mechanical function of muscle, and that in some cases a 1 nm difference can switch the net work of the half sarcomere model from motor like to brake like.

We derive a quantitative version of the hydrodynamic limit for an interacting particle system inspired by integrate-and-fire neuron models. More precisely, we show that the $L^2$-speed of convergence of the empirical density of states in a generalized contact process defined over a $d$-dimensional torus of size $n$ is of the optimal order $\mathcal O(n^{d/2})$. In addition, we show that the typical fluctuations around the aforementioned hydrodynamic limit are Gaussian, and governed by a inhomogeneous stochastic linear equation.

Interest in how democracies form consensus has increased recently, with statistical physics and economics approaches both suggesting that there is convergence to a fixed point in belief networks, but with fluctuations in opinions when there are ``stubborn'' voters. We modify a model of opinion dynamics in which agents are fully Bayesian to account for two cognitive biases: confirmation bias and in-group bias. Confirmation bias occurs when the received information is considered to be more likely when it aligns with the receiver's beliefs. In-group bias occurs when the receiver further considers the information to be more likely when the receiver's beliefs and the sender's beliefs are aligned. We find that when there are no cognitive biases, a network of agents always converges to complete consensus. With confirmation bias alone, polarization can occur. With both biases present, consensus and polarization are possible, but when agents attempt to counteract confirmation bias, there can be signatures of transient chaos and ongoing opinion fluctuations. Based on this simple model, we conjecture that complex opinion fluctuations might be a generic feature of opinion dynamics when agents are Bayesian with biases., Comment: 22 pages, 3 figures

Changes in the density and organization of fibrous biological tissues often accompany the progression of serious diseases ranging from fibrosis to neurodegenerative diseases, heart disease and cancer. However, challenges in cost, complexity, or precision faced by existing imaging methodologies pose barriers to elucidating the role of tissue microstructure in disease. Here, we leverage the intrinsic optical anisotropy of the Morpho butterfly wing and introduce Morpho-Enhanced Polarized Light Microscopy (MorE-PoL), a stain- and contact-free imaging platform which enhances and quantifies the birefringent material properties of fibrous biological tissues. We develop a mathematical model, based on Jones calculus, which quantifies fibrous tissue density and organization. As a representative example, we analyze collagen-dense and collagen-sparse human breast cancer tissue sections and leverage our technique to assess the microstructural properties of distinct regions of interest. We compare our results with conventional Hematoxylin and Eosin (H&E) staining procedures and second harmonic generation (SHG) microscopy for fibrillar collagen detection. Our findings demonstrate that our MorE-PoL technique provides a robust, quantitative, and accessible route toward analyzing biological tissue microstructures, with great potential for application to a broad range of biological materials.

Directed collective cell migration is central in morphogenesis, wound healing and cancer progression1,2. Although it is well-accepted that the molecular anisotropy of the micro-environment guides this migration3,4, its impact on the pattern of the cell flows remains largely unexplored. Studying confluent human bronchial epithelial cells (HBECs) in vitro, we show that subcellular microgrooves elicit a polar mode of collective migration in millimeter-long bidirectional lanes that are much wider than a cell size, even though cell flows are highly disordered on featureless surfaces 5. This directed flocking-like transition6,7 can be accounted for by a hydrodynamic theory of active polar fluids and corresponding numerical simulations. This model further predicts that anisotropic friction resulting from the grooves lowers the threshold of the transition, which we confirm experimentally. Therefore, microscopic anisotropy of the environment not only directs the collective motion of the cells in the easy direction, but also shapes the cell migration pattern. Flow patterns induced by collective contact guidance are thus markedly different from those induced by supracellular confinement8, demonstrating that all length-scales of the micro-environment must be considered in a comprehensive description of collective migration. Furthermore, artificial microtopographies designed from theoretical considerations can provide a rational strategy to direct cells to specific geometries and functions, which has broad implications, for instance, for tissue engineering strategies in organoid morphogenesis., Comment: Nature Physics, 2024

The tumor microenvironment (TME) plays a critical role in cancer cell proliferation, invasion, and resistance to therapy. A principal component of the TME is the tumor immune microenvironment (TIME), which includes various immune cells such as macrophages. Depending on the signals received from environmental elements like IL-4 or IFN-$\gamma$, macrophages can exhibit pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes. This study uses an enhanced agent-based model to simulate interactions within the TIME, focusing on the dynamic behavior of macrophages. We examine the response of cancer cell populations to alterations in macrophages, categorized into three different behaviors: M0 (initial-inactive), M1 (immune-upholding), and M2 (immune-repressing), as well as environmental differentiations. The results highlight the significant impact of macrophage modulation on tumor proliferation and suggest potential therapeutic strategies targeting these immune cells., Comment: 12 pages, 8 figures, 0 tables

Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans., Comment: Main text: 22 pages, 7 figures. Supplement: 18 pages, 10 figures

E. coli use a regular lattice of receptors and attached kinases to detect and amplify faint chemical signals. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in ligand concentration. These characteristics are well described by models which achieve their gain through equilibrium cooperativity. But these models are challenged by two experimental results. First, neither adaptation nor large gain are present in receptor binding assays. Second, in cells lacking adaptation machinery, fluctuations can sometimes be enormous, with essentially all kinases transitioning together. Here we introduce a far-from equilibrium model in which receptors gate the spread of activity between neighboring kinases. This model achieves large gain through a mechanism we term lattice ultrasensitivity (LU). In our LU model, kinase and receptor states are separate degrees of freedom, and kinase kinetics are dominated by chemical rates far-from-equilibrium rather than by equilibrium allostery. The model recapitulates the successes of past models, but also matches the challenging experimental findings. Importantly, unlike past lattice critical models, our LU model does not require parameters to be fine tuned for function.

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

We construct a hydrodynamic theory of active smectics A in two-dimensional space, including the creation/annihilation and motility of dislocations with Burgers' number $\pm1$. We derive analytical criteria on the set of parameters that lead to flows. We show that the motility of dislocations can lead to flow transitions with distinct features from the previously reported active Helfrich--Hurault shear instability with, notably, a first-order transition in the velocity from quiescence to turbulence., Comment: 5 pages, 5 figures

Usually, the strain-induced softening behaviour observed in the differential modulus $K(T,\gamma)$ of hydrogels has been attributed to the breakage of internal structures of the network, such as the cross-links that bind together the polymer chains. Here we consider a stress-strain relationship that we have recently derived from a coarse-grained model to demonstrate that no rupture of the network is needed for rubber-like gels to present such behaviour. In particular, we show that, in some cases, the decreasing of $K(T,\gamma)$ as a function of the strain $\gamma$ is closely related to the energy-related contribution to the elastic modulus that has been experimentally observed, e.g., for tetra-PEG hydrogels. Thus, our results suggest that, instead of the breakage of structures, the softening behaviour can be also related to the effective interaction between the chains in the network and their neighbouring solvent molecules. Comparison to experimental data determined for several hydrogels is included to illustrate that behaviour and to validate our approach., Comment: 10 pages, 4 figures

We review the construction and evolution of mathematical models of the Arabidopsis circadian clock, structuring the discussion into two distinct historical phases of modeling strategies: extension and reduction. The extension phase explores the bottom-up assembly of regulatory networks introducing as many components and interactions as possible in order to capture the oscillatory nature of the clock. The reduction phase deals with functional decomposition, distilling complex models to their essential dynamical repertoire. Current challenges in this field, including the integration of spatial considerations and environmental influences like light and temperature, are also discussed. The review emphasizes the ongoing need for models that balance molecular detail with practical simplicity.

Biophotonic nanostructures in butterfly wing scales remain fascinating examples of biological functional materials, with intriguing open questions in regards to formation and evolutionary function. One particularly interesting butterfly species, Erora opisena (Lycaenidae: Theclinae), develops wing scales that contain three-dimensional photonic crystals that closely resemble a single gyroid geometry. Unlike most other gyroid forming butterflies, E. opisena develops discrete gyroid crystallites with a pronounced size gradient hinting at a developmental sequence frozen in time. Here, we use a hyperspectral (wavelength-resolved) microscopy technique to investigate the ultrastructural organisation of these gyroid crystallites in dry, adult wing scales. We show that reflectance corresponds to crystallite size, where larger crystallites reflect green wavelengths more intensely; this relationship could be used to infer size from the optical signal. We further successfully resolve the red-shifted reflectance signal from wing scales immersed in refractive index oils with varying refractive index, including values similar to water or cytosol. Such photonic crystals with lower refractive index contrast may be similar to the hypothesized nanostructural forms in the developing butterfly scales. The ability to resolve these fainter signals hints at the potential of this facile light microscopy method for in vivo analysis of nanostructure formation in developing butterflies.

To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models. These models are computational frameworks that generate observable quantities from molecular configurations based on empirical relationships linking specific molecular properties to experimental measurements. Bayesian Inference of Conformational Populations (BICePs) is a reweighting algorithm that reconciles simulated ensembles with ensemble-averaged experimental observations, even when such observations are sparse and/or noisy. This is achieved by sampling the posterior distribution of conformational populations under experimental restraints as well as sampling the posterior distribution of uncertainties due to random and systematic error. In this study, we enhance the algorithm for the refinement of empirical forward model (FM) parameters. We introduce and evaluate two novel methods for optimizing FM parameters. The first method treats FM parameters as nuisance parameters, integrating over them in the full posterior distribution. The second method employs variational minimization of a quantity called the BICePs score that reports the free energy of `turning on` the experimental restraints. This technique, coupled with improved likelihood functions for handling experimental outliers, facilitates force field validation and optimization, as illustrated in recent studies (Raddi et al. 2023, 2024). Using this approach, we refine parameters that modulate the Karplus relation, crucial for accurate predictions of J-coupling constants based on dihedral angles between interacting nuclei. We validate this approach first with a toy model system, and then for human ubiquitin, predicting six sets of Karplus parameters. This approach, which does not rely on predetermined parameters, enhances predictive accuracy and can be used for many applications.

The emergent collective motion of active agents - in particular pedestrians - at a three-way intersection is studied by Langevin simulations of cognitive intelligent active Brownian particles (iABPs) with directed visual perception and self-steering avoidance. Depending on the maneuverability $\Omega$, the goal fixation $K$, and the vision angle $\psi$, different types of pedestrian motion emerge. At intermediate relative maneuverability $\Delta = \Omega/K$ and large $\psi$, pedestrians have noisy trajectories due to multiple scattering events as they encounter other pedestrians in their field of view. For $\psi = \pi$ and large relative maneuverability $\Delta$, an effectively jammed state is found, which belongs to the percolation universality class. For small $\psi$, agents exhibit localised clustering and flocking, while for intermediate $\psi$ self-organized rotational flows can emerge. The analysis of mean squared displacement and velocity auto-correlation of the agents reveals that the motion is well described by fractional Brownian Motion with positively correlated noise. Finally, despite the rich variety of collective behaviour, the fundamental flow diagram for the three-way-crossing setup shows a universal curve for the different vision angles. Our research provides valuable insights into the importance of vision angle and self-steering avoidance on pedestrian dynamics in semi-dense crowds., Comment: 9 figures

Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridges across time scales and enables a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish responding to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive stimuli, or in search for appetitive prey. As our method encodes the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies, we use it to uncover a hierarchical structure in the phenotypic variability that reflects exploration-exploitation trade-offs. Across a wide range of sensory cues, a major source of variation among fish is driven by prior and/or immediate exposure to prey that induces exploitation phenotypes. A large degree of variability that is not explained by environmental cues unravels motivational states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, by extracting the timescales of motor strategies deployed during navigation, our approach exposes structure among individuals and reveals internal states tuned by prior experience., Comment: 31 pages, 7 main figures, 1 supplementary table and 8 supplementary figures

Migration of animal cells is based on the interplay between actin polymerization at the front, adhesion along the cell-substrate interface, and actomyosin contractility at the back. Active gel theory has been used before to demonstrate that actomyosin contractility is sufficient for polarization and self-sustained cell migration in the absence of external cues, but did not consider the dynamics of adhesion. Likewise, migration models based on the mechanosensitive dynamics of adhesion receptors usually do not include the global dynamics of intracellular flow. Here we show that both aspects can be combined in a minimal active gel model for one-dimensional cell migration with dynamic adhesion. This model demonstrates that load sharing between the adhesion receptors leads to symmetry breaking, with stronger adhesion at the front, and that bistability of migration arises for intermediate adhesiveness. Local variations in adhesiveness are sufficient to switch between sessile and motile states, in qualitative agreement with experiments., Comment: Revtex, 31 pages, 7 figures

Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that exploit 3D structures of protein pockets were spotlighted for generating ligand molecules with their plausible binding modes. While docking scores superficially assess the quality of generated ligands, closer inspection of the binding structures reveals the inconsistency in local interactions between a pocket and generated ligands. Here, we address the issue by explicitly generating non-covalent interactions (NCIs), which are universal patterns throughout protein-ligand complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of protein-ligand edges along with a 3D graph of a ligand molecule during the sampling. With the NCI-generating strategy, our model generates ligands with more reliable NCIs, especially outperforming the baseline diffusion-based models. We further adopted inpainting techniques on NCIs to further improve the quality of the generated molecules. Finally, we showcase the applicability of NCIDiff on drug design tasks for real-world settings with specialized objectives by guiding the generation process with desired NCI patterns.

A recent experimental study reports on measuring the temporal duration and the spatial extent of failed attempts to cross an activation barrier (i.e., "loops") for a folding transition in a single molecule and for a Brownian particle trapped within a bistable potential. Within the model of diffusive dynamics, however, both of these quantities are, on the average, exactly zero because of the recrossings of the barrier region boundary. That is, an observer endowed with infinite spatial and temporal resolution would find that finite loops do not exist (or, more precisely, form a set of measure zero). Here we develop a description of the experiment that takes finite experimental resolution into account and show how the experimental uncertainty of localizing the point, in time and space, where the barrier is crossed leads to observable distributions of loop times and sizes. Although these distributions generally depend on the experimental resolution, this dependence, in certain cases, may amount to a simple resolution-dependent factor and thus the experiments do probe inherent properties of barrier crossing dynamics.

The tubulin-like protein FtsZ is crucial for cytokinesis in bacteria and many archaea, forming a ring-shaped structure called the Z-ring at the site of cell division. Despite extensive research, the formation of Z-rings is not entirely understood. We propose a theoretical model based on FtsZ's known filament structures, treating them as semiflexible polymers with specific mechanical properties and lateral inter-segment attraction that can stabilize ring formations. Our Langevin dynamics simulations reveal various morphological phases, including open helices, chains, rings, and globules, capturing experimental observations in the fission yeast model using FtsZ from different bacterial species or mutants of Escherichia coli. Using the theoretical model, we explore how treadmilling activity affects Z-ring stability and identify a spooling mechanism of ring formation. The active ring produces contractile, shear, and rotational stresses, which intensify before the Z-ring transitions to an open helix at high activity., Comment: 13 pages, 12 figures

Analyzing the motion of multiple biological agents, be it cells or individual animals, is pivotal for the understanding of complex collective behaviors. With the advent of advanced microscopy, detailed images of complex tissue formations involving multiple cell types have become more accessible in recent years. However, deciphering the underlying rules that govern cell movements is far from trivial. Here, we present a novel deep learning framework to estimate the underlying equations of motion from observed trajectories, a pivotal step in decoding such complex dynamics. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions based on the states of the interacting entities. We demonstrate the efficacy of our approach through two numerical experiments. First, we used a simulated data from a toy model to tune the hyperparameters. Based on the obtained hyperparameters, we then applied this approach to a more complex model that describes interacting cells of cellular slime molds. Our results show that the proposed method can accurately estimate the function of two-body interactions, thereby precisely replicating both individual and collective behaviors within these systems., Comment: 14 pages, 4 figures

Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-organization rules give rise to nearly all patterns in nature, such as a wide variety of Turing patterns, fractals, spiral, target and plane waves, as well as chaotic patterns. The symmetry breaking is rooted in basic physical quantities, such as positive and negative forces, space, time and bounds. Besides reproducing the hallmarks of complexity, we discover some novel phenomena, such as abrupt percolation of Turing patterns, phase transition between fractals and chaos, chaotic edge in travelling waves, etc. Our asymmetric self-organization theory established a simple and unified framework for the origin of complexity in all fields, and unveiled a deep relationship between the first principles of physics and the complex world, Comment: 97 pages, 60 figures, for full article, see https://doi.org/10.1142/S021812742430012X

Biological systems, particularly the brain, are frequently analyzed as networks, conveying mechanistic insights into their function and pathophysiology. This is the first study of a functional network of cardiac tissue. We use calcium imaging to obtain two functional networks in a subsidiary but essential pacemaker of the heart, the atrioventricular node (AVN). The AVN is a small cellular structure with dual functions: a) to delay the pacemaker signal passing from the sinoatrial node (SAN) to the ventricles, and b) to serve as a back-up pacemaker should the primary SAN pacemaker fail. Failure of the AVN can lead to syncope and death. We found that the shortest path lengths and clustering coefficients of the AVN are remarkably similar to those of the brain. The network is ``small-world," thus optimized for energy use vs transmission efficiency. We further study the network properties of AVN tissue with knock-out of the sodium-calcium exchange transporter. In this case, the average shortest path-lengths remained nearly unchanged showing network resilience, while the clustering coefficient was somewhat reduced, similar to schizophrenia in brain networks. When we removed the global action potential using principal component analysis (PCA) in wild-type model, the network lost its ``small-world" characteristics with less information-passing efficiency due to longer shortest path lengths but more robust signal propagation resulting from higher clustering. These two wild-type networks (with and without global action potential) may correspond to fast and slow conduction pathways. Laslty, a one-parameter non-linear preferential attachment model is a good fit to all three AVN networks., Comment: 14 pages, 8 figures at the end of the manuscript, two videos and three datasets in the Source folder

We report a model for hyperthermia therapies based on heat diffusion in a biological tissue containing a topological defect. Biological tissues behave like active liquid crystals with the presence of topological defects which are likely to anchor tumors during the metastatic phase of cancer evolution and the therapy challenge is to destroy the cancer cells without damaging surrounding healthy tissues. The defect creates an effective non-Euclidean geometry for low-energy excitations, modifying the bio-heat equation. Applications to protocols of thermal ablation for various biological tissues (liver, prostate, and skin) is analyzed and discussed.

The characterization of network and biophysical properties from neural spiking activity is an important goal in neuroscience. A framework that provides unbiased inference on causal synaptic interaction and single neural properties has been missing. Here we applied the stochastic dynamics extension of Maximum Entropy -- the Maximum Caliber Principle -- to infer the transition rates of network states. Effective synaptic coupling strength and neuronal response functions for various network motifs can then be computed. The inferred minimal model also enables leading-order reconstruction of inter-spike interval distribution. Our method is tested with numerical simulated spiking networks and applied to data from salamander retina.

In the past years, the amount of research on active matter has grown extremely rapidly, a fact that is reflected in particular by the existence of more than 600 review articles on this topic. Moreover, the field has become very diverse, ranging from theoretical studies of the statistical mechanics of active particles to applied work on medical applications of microrobots and from biological systems to artificial swimmers. This makes it very difficult to get an overview over the field as a whole. Here, we provide such an overview in the form of a metareview article that surveys the existing review articles on active matter. Thereby, this article provides an introduction to the various subdisciplines of active matter science and constitutes a useful starting point for finding literature about a specific topic., Comment: 28 pages, 2 figures, 646 references

The two-thirds power law is a link between angular speed $\omega$ and curvature $\kappa$ observed in voluntary human movements: $\omega$ is proportional to $\kappa^{2/3}$. Squared jerk is known to be a Lagrangian leading to the latter law. We propose that a broader class of higher-derivative Lagrangians leads to the two-thirds power law and we perform the Hamiltonian analysis leading to action-angle variables through Ostrogradski's procedure. In this framework, squared jerk appears as an action variable and its minimization may be related to power expenditure minimization during motion. The identified higher-derivative Lagrangians are therefore natural candidates for cost functions, i.e. movement functions that are targeted to be minimal when one individual performs a voluntary movement., Comment: 17 pages, 1 figure

An imbalance between bodily heat production and heat dissipation leads to heat stress in organisms. In addition to diminished animal well-being, heat stress is detrimental to the poultry industry as poultry entails fast growth and high yield, resulting in greater metabolic activity and higher body heat production. When stressed, cells overexpress heat shock proteins (such as HSP70, a well-established intracellular stress indicator) and may undergo changes in their mechanical properties. Liquid crystals (LCs, fluids with orientational order) have been recently employed to rapidly characterize changes in mechanical properties of cells enabling a means of optically reporting the presence of disease in organisms. In this work, we explore the difference in the expression of HSP70 to a change in the LC response pattern via the use of convolutional neural networks (CNNs). The machine-learning (ML) models were trained on hundreds of such LC-response micrographs of chicken red blood cells with and without heat stress. Trained models exhibited remarkable accuracy of up to 99% on detecting the presence of heat stress in unseen microscope samples. We also show that crosslinking the chicken and human RBCs using glutaraldehyde in order to simulate a diseased cell was an efficient strategy for planning, building, training, and evaluating ML models. Overall, our efforts build towards the rapid detection of disease in organisms, which is accompanied by a distinct change in the mechanical properties of cells. We aim to eventuate CNN-enabled LC-sensors can rapidly report the presence of disease in scenarios where human judgment could be prohibitively difficult or slow.

The intrinsic dynamics of most proteins are central to their function. Protein tyrosine kinases such as Abl1 undergo significant conformational changes that modulate their activity in response to different stimuli. These conformational changes constitute a conserved mechanism for self-regulation that dramatically impacts kinases' affinities for inhibitors. Few studies have attempted to extensively sample the pathways and elucidate the mechanisms that underlie kinase inactivation. Seeking to bridge this knowledge gap, we present a thorough analysis of the ``DFG flip'' inactivation pathway in Abl1 kinase. By leveraging the power of the Weighted Ensemble methodology, which accelerates sampling without the use of biasing forces, we have comprehensively simulated DFG flip events in Abl1 and its inhibitor-resistant variants, revealing a rugged landscape punctuated by potentially druggable intermediate states. Through our strategy, we successfully simulated dozens of uncorrelated DFG flip events distributed along two principal pathways, identified the molecular mechanisms that govern them, and measured their relative probabilities. Further, we show that the compound Glu255Lys/Val Thr315Ile Abl1 variants owe their inhibitor resistance phenotype to an increase in the free energy barrier associated with completing the DFG flip. This barrier stabilizes Abl1 variants in conformations that can lead to loss of binding for Type-II inhibitors such as Imatinib or Ponatinib. Finally, we contrast our Abl1 observations with the relative state distributions and propensity for undergoing a DFG flip of evolutionarily-related protein tyrosine kinases with diverging Type-II inhibitor binding affinities. Altogether, we expect that our work will be of significant importance for protein tyrosine kinase inhibitor discovery., Comment: 34 pages, 10 figures

Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted $3$-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.

Chemical affinities are responsible for driving active matter systems out of equilibrium. At the nano-scale, molecular machines interact with the surrounding environment and are subjected to external forces. The mechano-chemical coupling which arises naturally in these systems reveals a complex interplay between chemical and mechanical degrees of freedom with strong impact on their active mechanism. By considering various models far from equilibrium, we show that the tuning of applied forces give rise to a nonlinear response that causes a non-monotonic behaviour in the machines' activity. Our findings have implications in understanding, designing, and triggering such processes by controlled application of external fields, including the collective dynamics of larger non-equilibrium systems where the total dissipation and performance might be affected by internal and inter-particle interactions.

We study the role of active coupling on the transport properties of homogeneously charged macromolecules in an infinitely dilute solution. An enzyme becomes actively bound to a segment of the macromolecule, exerting an electrostatic force on it. Eventually, thermal fluctuations cause it to become unbound, introducing active coupling into the system. We study the mean-squared displacement (MSD) and find a new scaling regime compared to the thermal counterpart in the presence of hydrodynamic and segment-segment electrostatic interactions. Furthermore, the study of segment-segment equal-time correlation reveals the swelling of the macromolecule. Further, we derive the concentration equation of the macromolecule with active binding and study how the cooperative diffusivity of the macromolecules get modified by its environment, including the macromolecules itself. It turns out that these active fluctuations enhance the effective diffusivity of the macromolecules. The derived closed-form expression for diffusion constant is pertinent to the accurate interpretation of light scattering data in multi-component systems with binding-unbinding equilibria., Comment: 13 pages, 1 figure

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components -- the so-called agents -- discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment., Comment: 32 pages, 12 Figures

We consider a run-and-tumble particle whose speed and tumbling rate are space-dependent on an infinite line. Unlike most of the previous work on such models, here we make the physical assumption that at large distances, these rates saturate to a constant. For our choice of rate functions, we show that a stationary state exists, and the exact steady state distribution decays exponentially or faster and can be unimodal or bimodal. The effect of boundedness of rates is seen in the mean-squared displacement of the particle that displays qualitative features different from those observed in the previous studies where it approaches the stationary state value monotonically in time; in contrast, here we find that if the initial position of the particle is sufficiently far from the origin, the variance in its position either varies nonmonotonically or plateaus before reaching the stationary state. These results are captured quantitatively by the exact solution of the Green's function when the particle has uniform speed but the tumbling rates change as a step-function in space; the insights provided by this limiting case are found to be consistent with the numerical results for the general model.

We consider a dynamic system that is driven by an intensity-modulated Poisson process with intensity $\Lambda(t)=\lambda(t)+\varepsilon\nu(t)$. We derive an exact relation between the input-output cross-correlation in the spontaneous state ($\varepsilon=0$) and the linear response to the modulation ($\varepsilon>0$). This can be regarded as a variant of the Furutsu-Novikov theorem for the case of shot noise. As we show, the relation is still valid in the presence of additional independent noise. Furthermore, we derive an extension to Cox-process input, i.e. to colored shot noise. We discuss applications to particle detection and to neuroscience. Using the new relation, we obtain a fluctuation-response-relation for a leaky integrate-and-fire neuron. We also show how the new relation can be used in a remote control problem in a recurrent neural network. The relations are numerically tested for both stationary and non-stationary dynamics. Lastly, extensions to marked Poisson processes and to higher-order statistics are presented., Comment: 12 pages, 9 figures

Overflow metabolism is a ubiquitous phenomenon whereby cells in aerobic conditions excrete byproducts of glycolysis, such as lactate or acetate, into the medium in a seemingly wasteful and polluting fashion. Whilst overflow may confer microbes a fitness advantage by allowing them to overcome a finite oxidative capacity, its occurrence in higher organisms is harder to assess. Important insight was however obtained in recent experiments conducted at single-cell resolution, which revealed that accumulation of overflow products in tumor cell cultures known as the Warburg effect arises from imbalances in the dynamic and heterogeneous inter-cellular exchange network through which cells collectively regulate the microenvironment. Here we provide a quantitative characterization of this scenario by integrating metabolic network modeling with diffusion constraints, statistical physics theory and single-cell experimental flux data. On the theoretical side, we clarify how diffusion-limited exchanges shape the space of viable metabolic states of a multi-cellular system. Specifically, a phase transition from a balanced network of exchanges to an unbalanced overflow regime occurs as the mean cellular glucose and oxygen uptakes vary while single-cell metabolic phenotypes are highly heterogeneous around this transition. We then show that time-resolved data from human tumor-stroma cell co-cultures consistently map to this crossover region, supporting the idea that environmental deterioration reflects a failure of coordination among recurrently interacting cells. In summary, our findings suggest that, rather than deriving from multiple independent cell-autonomous processes, environmental control is an emergent feature of multi-cellular systems., Comment: Main(14 pages)+ supporting information(14 pages). Comments are welcome

This paper articulates metacognition using the language of statistical physics and Bayesian mechanics. Metacognitive beliefs, defined as beliefs about beliefs, find a natural description within this formalism, which allows us to define the dynamics of 'metacognitive particles', i.e., systems possessing metacognitive beliefs. We further unpack this typology of metacognitive systems by distinguishing passive and active metacognitive particles, where active particles are endowed with the capacity for mental actions that update the parameters of other beliefs. We provide arguments for the necessity of this architecture in the emergence of a subjective sense of agency and the experience of being separate from the environment. The motivation is to pave the way towards a mathematical and physical understanding of cognition -- and higher forms thereof -- furthering the study and formalization of cognitive science in the language of mathematical physics., Comment: 13 pages, 4 figures

We present a theoretical investigation of the electronic specific heat (ESH) at constant volume (Cv) of single-helical proteins modeled within the tight-binding (TB) framework. We study the effects of helical symmetry, long-range hopping, environment and biological defects on thermal properties. We employ a general TB model to incorporate all parameters relevant to the helical structure of the protein. In order to provide additional insights into our results for the ESH, we also study the electronic density of states for various disorder strengths. We observe that the variation of the specific heat with disorder is very different in low and high temperature regimes, though the variation of ESH with temperature possesses a universal pattern upon varying disorder strengths related to environmental effects. Lastly, we propose an interesting application of the ESH spectra of proteins. We show that by studying the ESH of single-helical proteins, one can distinguish a defective sample from a pure one. This observation can serve as the basis of a screening technique that can be applied prior to a whole genome testing, thereby saving valuable time & resources.

The upcoming phase of space exploration not only includes trips to Mars and beyond, but also holds great promise for human progress. However, the vulnerability of space habitats to cosmic radiation, which consists of Galactic Cosmic Rays and Solar Particle Events, raises important safety concerns for astronauts and other living things that will accompany them. Research exploring the biological effects of cosmic radiation consists of experiments conducted in space itself and in simulated space environments on Earth. Notably, NASA's Space Radiation Laboratory has taken significant steps forward in simulating cosmic radiation by using particle accelerators, marking a notable advancement in this field. Intriguingly, much of the research emphasis thus far has been on understanding how cosmic radiation impacts living organisms, instead of finding ways to help them resist the radiation. In this paper, we briefly talk about current research on the biological effects of cosmic radiation and propose possible protective measures through biological interventions. In our opinion, biological pathways responsible for coping with stressors on Earth offer potential solutions for protection against the stress caused by cosmic radiation. Additionally, we recommend assessing the effectiveness of these pathways through experiments using particle accelerators to simulate the effects of cosmic radiation.

In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs. However, recent studies have indicated that these ML models may overfit to quantitative metrics while neglecting the physical constraints inherent in the problem. In this work, we present Uni-Mol Docking V2, which demonstrates a remarkable improvement in performance, accurately predicting the binding poses of 77+% of ligands in the PoseBusters benchmark with an RMSD value of less than 2.0 {\AA}, and 75+% passing all quality checks. This represents a significant increase from the 62% achieved by the previous Uni-Mol Docking model. Notably, our Uni-Mol Docking approach generates chemically accurate predictions, circumventing issues such as chirality inversions and steric clashes that have plagued previous ML models. Furthermore, we observe enhanced performance in terms of high-quality predictions (RMSD values of less than 1.0 {\AA} and 1.5 {\AA}) and physical soundness when Uni-Mol Docking is combined with more physics-based methods like Uni-Dock. Our results represent a significant advancement in the application of artificial intelligence for scientific research, adopting a holistic approach to ligand docking that is well-suited for industrial applications in virtual screening and drug design. The code, data and service for Uni-Mol Docking are publicly available for use and further development in https://github.com/dptech-corp/Uni-Mol.

The spread of microbial infections is governed by the self-organization of bacteria on surfaces. Limitations of live imaging techniques make collective behaviors in clinically relevant systems challenging to quantify. Here, we develop novel experimental and image analysis techniques for high-fidelity single-cell segmentation of bacterial colonies. Machine learning-based segmentation models are trained solely using synthetic microscopy images that are processed to look realistic using state-of-the-art image-to-image translation methods, requiring no biophysical modeling. Accurate single-cell segmentation is achieved for densely packed single-species colonies and multi-species colonies of common pathogenic bacteria, even under suboptimal imaging conditions and for both brightfield and confocal laser scanning microscopy. The resulting data provide quantitative insights into the self-organization of bacteria on soft surfaces. Thanks to their high adaptability and relatively simple implementation, these methods promise to greatly facilitate quantitative descriptions of bacterial infections in varied environments., Comment: 11 pages, 5 figures

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice.

Entropy production (EP) is a central quantity in nonequilibrium physics as it monitors energy dissipation, reversibility, and free energy differences during thermodynamic transformations. Estimating EP, however, is challenging both theoretically and experimentally due to limited access to the system dynamics. For overdamped Langevin dynamics and Markov jump processes it was recently proposed that, from thermodynamic uncertainty relations (TUR), short-time cumulant currents can be used to estimate EP without knowledge of the dynamics. Yet, estimation of EP in underdamped Langevin systems remains an active challenge. To address this, we derive a modified TUR that relates the statistics of two specific novel currents -- one cumulant current and one stochastic current -- to a system's EP. These two distinct but related currents are used to constrain EP in the modified TUR. One highlight is that there always exists a family of currents such that the uncertainty relations saturate, even for long-time averages and in nonsteady-state scenarios. Another is that our method only requires limited knowledge of the dynamics -- specifically, the damping-coefficient to mass ratio and the diffusion constant. This uncertainty relation allows estimating EP for both overdamped and underdamped Langevin dynamics. We validate the method numerically, through applications to several underdamped systems, to underscore the flexibility in obtaining EP in nonequilibrium Langevin systems., Comment: 14 pages, 5 figures; https://csc.ucdavis.edu/~cmg/compmech/pubs/EstimateEP.htm

Using the Lebwohl-Lasher interaction for reciprocal local alignment, we present a comprehensive phase diagram for a dry, apolar, active nematic system using its stochastic dynamics. The nematic-isotropic transition in this system is first-order and fluctuation-dominated. Our phase diagram identifies three distinct regions based on activity and orientational noise relative to alignment strength: a homogeneous isotropic phase, a nematic phase with giant density fluctuations, and a coexistence region. Using mean-field analysis and hydrodynamic theory, we demonstrate that reciprocal interactions lead to a density fluctuation-induced first-order transition and derive a phase boundary consistent with numerical results. Quenching from the isotropic to nematic phase reveals coarsening dynamics with distinctive scaling behaviors for density and nematic fields, exhibiting exponents between 2 and 3, typical of non-conserved and conserved dynamics., Comment: 9 pages, 3 figures

The motility-induced phase separation (MIPS) phenomenon in active matter has been of great interest for the past decade or so. A central conceptual puzzle is that this behavior, which is generally characterized as a nonequilibrium phenomenon, can yet be explained using simple equilibrium models of thermodynamics. Here, we address this problem using a new theory, statistical teleodynamics, which is a conceptual synthesis of game theory and statistical mechanics. In this framework, active agents compete in their pursuit of maximum effective utility, and this self-organizing dynamics results in an arbitrage equilibrium in which all agents have the same effective utility. We show that MIPS is an example of arbitrage equilibrium and that it is mathematically equivalent to other phase-separation phenomena in entirely different domains, such as sociology and economics. As examples, we present the behavior of Janus particles in a potential trap and the effect of chemotaxis on MIPS., Comment: arXiv admin note: substantial text overlap with arXiv:2312.05765

Active matter concerns many-body systems comprised of living or self-driven agents that collectively exhibit macroscopic phenomena distinct from conventional passive matter. Using Schwinger-Keldysh effective field theory, we develop a novel hydrodynamic framework for thermal active matter that accounts for local temperature variations and the ensuing stochastic effects. This framework provides a deeper understanding of energy balance, second law of thermodynamics, and thermostated steady states in active matter, while also addressing the systematic violations of fluctuation-dissipation theorem and detailed balance. We use our framework of active hydrodynamics to develop effective field theory actions for active superfluids and active nematics that offer a first-principle derivation of various active transport coefficients and feature activity-induced phase transitions.