Frontiers in Physics

Non-exponential residence times give rise to history-dependent or memory effects in epidemic dynamics, yet their mathematical formulations remain fragmented. Here we develop a unified framework for a history-dependent SEIR model with general latent- and infectious-period distributions. We first establish the equivalence between the stage-age and renewal formulations and show that both admit a unified transition-flow representation. We then introduce the concept of a transition-flow multiplier, which provides a direct link between residence-time distributions and transition flows. When the multiplier is a weighted sum of shifted power functions, the model admits an exact representation in terms of Riemann-Liouville or tempered Riemann-Liouville fractional operators, providing a first-principles foundation for fractional epidemic models. Finally, using Euler-Lotka equations and stochastic-order arguments, we characterize how the latent- and infectious-period distributions shape epidemic growth and decay. A longer latent period slows both early growth and late decay, while, at fixed means, greater variability in the latent period accelerates early growth but slows late decay, whereas greater variability in the infectious period slows both.

Background: Long-term mortality data among Brazilian police officers and firefighters are scarce. This study investigated the leading causes of death in Brazilian male military police officers and firefighters over three decades, comparing their mortality and mean age at death with those of the male population. Methods: We retrospectively analyzed death records of male military police officers and firefighters from Espirito Santo (ES), Brazil, from 1988 to 2019. Mortality was analyzed across three decades and the standardized mortality ratios (SMRs) for the three leading causes were calculated using the male populations of ES and Brazil as references. Results: Overall, 2,198 deaths were identified, with a mean age at death of 59 {+/-} 18.5 years, compared with 60.5 {+/-} 23.9 years among men in ES and 63.4 {+/-} 23.7 years among Brazilian men. Mean age at death was 36 {+/-} 9 years among active-duty and 68 {+/-} 13 years among inactive personnel. Across the study population, the leading causes of death were circulatory system diseases (32%), external causes (26%) and neoplasms (15%). Circulatory disease mortality was higher in the second and third decades compared with both reference populations, whereas external-cause mortality shifted from higher, in the first two decades, to lower than expected in the last decade. Neoplasm mortality was higher relative to Brazil only in the last decade. Conclusion: Mortality patterns differed by cause and over time. The younger mean age at death among active-duty personnel, together with cause-specific excess mortality, highlights the importance of long-term occupational health surveillance and preventive strategies.

Humans and other animals live in interconnected social communities that are essential for survival and well-being. In principle, social communities can persist despite changes in constituent members. However, gradually accumulating changes to membership could push systems past critical thresholds into alternate states. We investigated social network dynamics for 443 wild female Asian elephants over 12 years by tracking community membership and structure. Following accumulated demographic turnover, networks fractured into smaller and more isolated social communities. Connections were changed not only due to turnover of members but also rewired among remaining individuals. This provides the first empirical evidence of a social network state change in a long-lived mammal.

We investigate the dynamics of the spread of the infection due to a phage- during the range expansion of an E coli colony. We find a rich panoply of spatio-temporal dynamics at large colony-level length scales, arising due to the interplay of mechanical forces, growth and viral infection dynamics at smaller cellular length scales. Strikingly, the phages "surf" the front of the growing bacterial colony by hitchhiking on E coli that are advected due to the growth of bacteria in the colony, resulting in an anisotropic spread of the phages. We identify microscopic processes - of the phage release during lysis (occurring on short millisecond timescales at length scales close to that of a single bacterium) and local nematic alignment of the rod-like E coli bacteria (occurring on timescales comparable to the bacterial growth rate and the length scales of a few bacterial cells) - that enhance the advective effects driving the hitchhiking behavior. Combining our experiments with a mathematical model, we explain the multiple spatio-temporal dynamical regimes - from the coexistence of uninfected bacteria, resistant (lysogenic) cells and phages, to fixed points where the entire population turns resistant or remains uninfected. Our work broaches a new frontier in the investigation of eco-evolutionary processes arising from the mechanical and ecological forces due to the interactions between viruses and their hosts.

Despite lacking a dedicated structural function, brain tissue is exposed to severe mechanical loading during accidents and trauma, with potentially permanent or lethal consequences. Therefore, characterizing its mechanical behavior assumes particular importance for designing protective equipment, planning surgeries, and training neurosurgeons, tasks that often rely on physical brain replicas made from phantom materials. Although the mechanics of brain tissue has been extensively studied, the selection of suitable phantom materials still relies on simplified metrics that fail to capture the highly nonlinear and rate-dependent behavior of the brain. In this work, we characterize the mechanical response at low-to-medium strain rates of common synthetic brain phantom materials, including Sylgard 527, Ecoflex Gel, and a composite hydrogel, and establish rigorous comparisons with porcine brain tissue as a benchmark. To this end, we employ a multi-step stress relaxation protocol under both uniaxial extension and compression across two different strain rates, introduce thermodynamic metrics, and perform comparisons based on rigorous statistical analysis. In particular, we isolate the thermodynamic equilibrium stress-strain curves, representing the baseline material's response, from rate-dependent dissipative inelastic effects, relatable to viscoelastic and poroelastic mechanisms. Our results provide a thorough comparison, revealing critical limitations in existing simulant materials and highlighting significant space for developing improved brain surrogates. The comprehensive dataset collected can also support the calibration of constitutive models, enhancing the predictive accuracy of numerical simulations involving brain tissue and its surrogates. More broadly, this study highlights the need to move beyond simplistic, scalar-metric-based comparisons of mechanical responses across materials.

Host immunity acquired through prior infections is a key factor shaping the spread of emerging variants. However, accounting for complete infection histories in mathematical models is challenging because the number of possible histories grows exponentially with the number of variants. Here, we present a flexible framework for modeling the co-circulation of multiple variants emerging at arbitrary times while incorporating cross-immunity and variant-specific characteristics. We introduce three tractable approximations: truncation, which retains only infection histories up to a given number of strains; one variant at a time, which is exact when variants circulation does not overlap; and two variants at a time, which is exact when at most two strains co-circulate. We systematically evaluate these approximations across a range of cross-immunity levels and inter-variant emergence delays. Truncation approximations perform best under short emergence delays and strong cross-immunity, whereas the other approaches outperform them at longer delays. When applied to SARS-CoV-2 variant dynamics, the second-order truncation and the two variants at a time approximation accurately capture incidence patterns, whereas the other approaches perform well only for specific subsets of variants. These results identify the parameter regimes in which simplifying assumptions remain valid, provide guidance for model selection, and highlight the key role of infection history in shaping epidemic trajectories.

Dense, well-ordered material states of proteins form the amyloid fibers that are a hallmark of neurodegenerative disease in the brain. Beyond forming amyloid fibers, some of these proteins can also adopt other material states termed condensates which are initially liquid-like but evolve to a soft, glassy phase. Fiber growth requires a large supply of monomers and, thus, it is often speculated that fibers emerge from within a dense condensate as it ages and its microscopic dynamics slow into a glassy state. Here, we use the well-established model system Fused in Sarcoma (FUS) to directly observe, quantify and theoretically describe fiber growth and its interplay with condensates. We report the discovery that fibers grow overwhelmingly in the dilute phase surrounding the condensates while the condensates concurrently evolve to a glassy arrested solid. The resulting protein fibers and glassy condensates are both distinct solid-like phases that coexist but do not directly interconvert. Taken together, these findings reveal that there are two competitive aging pathways in FUS condensation that are linked through phase separation kinetics.

Background: Mounting evidence shows war directly and indirectly harms civilian health. As the largest conflict since World War II, the war in Ukraine severely impacts civilian survival. Stroke--a leading cause of death in Ukraine--represents a key population-level vulnerability to full-scale war. We assessed the war's impact on population-level stroke mortality. Methods: This retrospective cohort study evaluated patients diagnosed with hemorrhagic or ischemic stroke admitted to Ukrainian civilian hospitals from April 1, 2020, through December 31, 2024. Using data from the Ukrainian nationwide electronic health record system, we compared pre-war and post-war stroke mortality. We used multivariate proportional hazards regression with propensity matching to test if in-hospital mortality increased. Excess deaths were estimated by multiplying the pre-post mortality rate difference by the post-period population in person-days. Results: Among 577,501 total individuals, we found that the crude in-hospital stroke mortality rate increased by 117 (95% CI: 94.2-139.9) per 100,000 person-days in the post-war period. In propensity-matched analyses, the mortality risk was 5% (HR=1.05; 95% CI: 1.03-1.06; p<0.001) higher in the post-war period. Mortality risk varied by age, sex, admission type and stroke type. Patients with hemorrhagic stroke had 179% (HR=2.79; 95% CI: 2.75-2.83; p<0.001) higher risk of death compared to ischemic stroke patients. Conclusion: In the first study to assess stroke mortality at the population level in Ukraine after the start of the full-scale war, we found that in-hospital mortality increased in the post-war period with an estimated 3,944 excess stroke deaths.

Cells use ATP-driven chaperones to extract clients from biomolecular condensates selectively, yet no closed-form criterion links molecular parameters to condensate quality control. We derive a minimal non-equilibrium sorting reactor model that reduces the reaction-diffusion dynamics to one algebraic equation. In the idealized low-load limit, a concentration-dependent partition coefficient produces a saddle-node bifurcation between functional clearance and pathological storage. The critical Damk\"ohler number is \(\mathrm{Da}_{0c}=e^{-2}\) and is independent of the feedback strength. Corrections shift the value but preserve the topology. The model predicts a Kramers-type functional lifetime and recovery costs that scale as \(R^3\). Critical slowing down near the bifurcation provides early-warning signatures for FCS and FRAP.

We develop a general geometrical framework for growth processes in which the growth rate depends exponentially on the number of active growth-supporting components. The framework separates the fraction of active components from the size of the accessible component space, allowing qualitatively different growth laws to arise from the same underlying construction. Bacterial population growth provides an example of a fixed-size component space controlled primarily by activation and deactivation, whereas examples such as human population growth or speciation provide an expanding component space in which interactions between individuals promote further growth. This motivates a Replicative geometrical law, dX/dt= X exp({beta} X^{gamma}), whose exponent characterizes the scaling of the effective growth-supporting component space and is tested against historical human-population regimes and eukaryotic lineage-through-time data. For innovation indicators such as patents and arXiv manuscript submissions, where an existing item need not itself reproduce, the same construction motivates a Direct Geometrical production law without the prefactor X. The framework therefore provides a common description of fixed and expanding component spaces while distinguishing Replicative from Direct Geometrical growth.

Phase separating biopolymers are often described by the sticker-spacer model, which accounts for non-attractive linkers that connect discrete attractive moieties. "Associative polymer" theories describe these systems as two coexisting fluids of stickers and spacers, respectively. In such theories the stickers and spacers are decoupled in the sense that sticker binding does not affect spacer conformations or vice versa. Informed by coarse-grained simulations, we develop a phenomenological mean-field theory to determine the microscopic coupling between the sticker and spacer degrees of freedom. Accounting for linkers connecting adjacent spacers introduces free energy contributions due to conformational entropy of the spacer and the binding combinatorics of the stickers. These contributions have opposite dependencies on spacer length because longer spacers allow a greater volume to search for binding partners but incur a greater entropy cost upon bond formation. The length dependent terms from these contributions cancel leaving a length-independent elastic term arising from the fact that a small amount of spacer stretching lowers the free energy by increasing the number of possible binding partners. The cancellation of spacer length from the theory justifies the decoupling approximation and supports mean-field treatments of heterogeneous spacers.

Biomolecular condensates are condensed assemblies of biomolecules that form through the process of liquid phase separation. Condensates in biology typically function as membraneless organelles, providing compartmentalization in the absence of a dividing lipid membrane. The molecular make-up of different condensates includes diverse multivalent proteins and nucleic acids as the primary drivers, many of them including significant fractions of intrinsically disordered regions (IDRs). To date, the sequence to phase separation relationship of IDRs has focused largely on one protein at a time, studying single-component condensate formation, or single-component partitioning into condensates. The co-phase separation and mixing of two or more IDRs is considerably more complex as both sequences can vary widely in their self- and cross-interactions, as well as their relative abundance in solution. It is unclear that a rule which predicts how a single sequence behaves will also predict what happens when two sequences are mixed. In this study, we disentangle the influence of sequence from that of composition using a set of 18 LAF-1 RGG variants that keep the same length and amino-acid composition and change only the order of the residues. This lets us vary charge patterning and, to a lesser extent, hydropathy patterning while keeping protein composition fixed. By themselves, the sequences phase separation and single-chain compaction are controlled by their degree of charge and hydropathy patterning. Within single-component condensed phases, each sequence adopts a more extended conformational ensemble, due to a more favorable, selfsolvated environment. We find that mixing two IDRs together into a condensate causes this universal scaling behavior to break, impacted by the relative interactions of the two components and overall composition of the slab. We find two different qualitative behaviors, one characterized by cooperative co-condensation when both sequences are subcritical, and the other by scaffold-client behavior when one sequence is supercritical. The scaffold-client systems generally show a high degree of demixing, while the co-condensing systems are generally quite well-mixed in the dense phase. This is surprising because even in cases where both partners have significantly different patterning parameters, they still mix. Thus, the descriptor that predicts a sequence's behavior alone can help indicate whether it will mix with or separate from a second component, but it does not fully determine the outcome on its own.

It is important to determine whether native state dynamics drive the misfolding and oligomerization of the prion protein, which are important events in prion disease, and how they are modulated by conformational conversion. Native (N) mouse prion protein (moPrP) is known to form small (OS) and large (OL) oligomers rich in {beta}-sheet, and in this study, photoinduced electron transfer-fluorescence correlation spectroscopy (PET-FCS) has been used to characterize intramolecular dynamics within individual monomeric units in both isolated OS and OL, as well as the diffusion properties of the oligomers. It is estimated that OS and OL comprise of about 15 and 55 monomeric units, respectively. Microsecond dynamics at each of the two regions that are the 1-3 and 2-3 interfaces of native protein are distinct in N, OS and OL, although they occur on very similar timescales. Analysis of the evolution of the distribution of diffusion times, determined using the maximum entropy method, indicates heterogeneity in the oligomerization reaction. Analysis of the change in the fluctuations which occur in two different timescales in the native state ensemble shows that they are damped more at the erstwhile 1-3 interface than the erstwhile 2-3 interface. The difference in the extent of damping at the erstwhile 1-3 and 2-3 interfaces can be explained on the basis of the structural changes known to occur across each region. The changes in dynamics occur concurrently in both regions, indicating that the structural changes accompanying conformational conversion also occur simultaneously during the oligomerization of moPrP.

The 2026 Andes virus (ANDV) outbreak aboard the MV Hondius cruise ship resulted in 11 confirmed cases and 3 deaths and raised questions regarding the origin of infection, with much media focus resting on a landfill in Tierra del Fuego. We evaluated competing exposure hypotheses by assembling open data on reservoir host occurrences, rodent serosurveys, and historical ANDV outbreaks along with the index case's self-documented, publicly-posted citizen-science digital record of their movements. Historical outbreaks associated with human-to-human transmission were concentrated within a restricted region spanning southern Chile and adjacent western Argentina, coinciding with the distribution of the principal reservoir host, Oligoryzomys longicaudatus, and areas identified as elevated risk by multiple ecological and genetic models. The index case was documented within this transmission focus shortly before the putative exposure period. Together, our open data approach was able to rapidly locate initial exposure within the established ANDV transmission focus and develop scenarios to prioritise further epidemiological investigation.

Nanobubbles (NBs) have been widely investigated as ultrasound contrast and therapeutic agents, yet whether submicron gas bubbles can directly generate measurable ultrasound signals remains controversial. Because acoustic scattering scales with bubble size, several studies have recently attributed nanobubble echogenicity primarily to larger bubbles. A recent Science Advances publication, attributed the loss of ultrasound signal after filtration to the removal of residual larger bubbles [1]. This interpretation assumes that filtration acts solely as a size-selection process. Here, we show that filtration also alters the shell properties and stability of lipid-coated NBs, independent of size. Filtered NBs (F-NBs) show significant lipid loss and accumulation on the filters themselves, resulting in decreased bubble stability and greater susceptibility to destruction under acoustic excitation. To decouple size from processing effects, we generated NB populations size-matched to filtered samples using increased centrifugation (IC-NBs), a comparatively gentler separation method. Despite comparable size distributions, IC-NBs retained sustained ultrasound scattering, whereas filtered NBs showed significantly reduced acoustic persistence. Filtration through 5 m pores, ~5-fold larger than the largest bubbles, preserved acoustic persistence but still reduced bubble concentration by ~30%, suggesting that membrane contact may contribute to bubble loss beyond size exclusion. Complementary lipid and gas-content assays provided further evidence of filtration-induced changes. The 0.45 m filtration removed substantially more total lipid than could be attributed to free-lipid removal alone, while the lipid associated with the larger bubbles removed during filtration accounted for only ~5% of the additional lipid loss. Encapsulated gas content remained comparable to that of size-matched centrifuged controls, suggesting that the reduced acoustic stability of filtered NBs is more closely associated with changes in lipid content than with loss of the gas core. These results indicate that membrane filtration is not a purely size-selective process: it removes substantially more lipid than size exclusion predicts and yields bubbles that are less stable under ultrasound, most likely through changes in shell lipid content or in the surrounding lipid reservoir, underscoring the importance of processing-induced effects in interpreting nanobubble echogenicity.

Quantitative analysis of biological shape is central to understanding how form encodes function. Despite rapid advances in volumetric imaging and segmentation, analysis and of three-dimensional morphology remains challenging due to complex geometries and topologies found in biology. Existing approaches either rely on handcrafted metrics that lack generalizability or employ data-driven neural models that sacrifice interpretability and robustness. Here, we introduce Minkowski Tensors (MT), a family of integral-geometric shape descriptors that extend classical scalar measures of volume, surface area, and curvature into tensor-valued quantities encoding direction and anisotropy. MT form a provably complete yet compact and interpretable feature set that captures volumetric and curvature-based information in a transformation- and scale-covariant, topology-aware manner. We benchmark MT on diverse segmented 3D biomedical images spanning vascular and adrenal structures from magnetic resonance angiography (MRA) and computed tomography (CT), and dividing cell nuclei imaged by confocal microscopy, demonstrating competitive or superior classification performance relative to classical geometric descriptors and neural baselines while retaining direct biological interpretability. Our results establish Minkowski tensors as a concise yet powerful framework for discriminative analysis of complex 3D biological morphology.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.

The actin cytoskeleton drives shape changes and transport in cells and supports mechanical signal transmission. However, how cells control and use active cytoskeletal flows at the mesoscale is not well understood. We reconstituted an active cytoskeleton in water-in-oil emulsion droplets of Xenopus laevis egg extract and observed, above a critical droplet size, the emergence of a 3D radially convergent steady-state flow of polymeric actin, maintained by continuous actin turnover. The flow condensed lipid-rich cellular debris into a centered inclusion. Steady-state F-actin density and flow velocity profiles roughly collapse onto scale-invariant master curves. This behavior can be explained by a physical model representing the network as an isotropic active viscous fluid with a percolation threshold. The contracting network behaves as an active swimmer with complex internal dynamics that centers itself and the central inclusion inside the droplets without physical boundary attachment. Active contraction, crosslinking and polymerization dynamics in an actin network can thus generate cell-scale flow patterns that sense the confining geometry and external signals and exert forces that are likely sufficient to move and localize organelles in cells.

Simulation models are widely used to support counterfactual inference in settings where direct experimental validation is difficult or infeasible. However, for phenomenological models calibrated to observational time series, goodness-of-fit does not necessarily establish that the structural assumptions underlying such inferences are uniquely supported by the data. Here, we examine this issue using a published COVID-19 transmission model for Japan that incorporates empirical inputs including human mobility, variant prevalence, and vaccination and has been used to estimate the effects of alternative vaccination scenarios. Using the publicly archived code together with a reconstructed substitute for a restricted-access input file required by the released implementation, we first reproduced the published model behaviour. We then performed two complementary computational stress tests. First, the vaccination-related input was suppressed and the remaining parameters were re-estimated against the original epidemic trajectory. Second, as a complementary test of trajectory-level flexibility, the model was refitted to synthetic target series with qualitatively different temporal structures while retaining the empirical mobility and variant inputs. The model retained substantial fitting capacity in both tests: suppression of the vaccination-related input caused only a limited deterioration in fit to the original trajectory, while qualitatively different synthetic trajectories could also be reproduced through parameter adjustment. Variation in fitted parameters across these conditions was also consistent with compensatory parameter behaviour. These results do not imply that vaccination had no effect or that the original model is invalid. Rather, they provide empirical evidence that, in this case, goodness-of-fit to the observed epidemic trajectory alone does not uniquely support the structural interpretation required for vaccination-related counterfactual inference. The study illustrates the value of reproducible computational stress testing as a complement to, rather than a substitute for, formal identifiability or model-comparison analyses.

Collective migration is often attributed to complex cell-cell communication, yet coordinated multicellular dynamics can also emerge in organisms lacking social interactions. Continuum descriptions successfully capture such behaviors but generally rely on phenomenological transport coefficients whose microscopic origin remains elusive. Here we show that these coefficients emerge from cell-cell collisions and can be independently determined from experiments. We investigate the aerotactic dispersal of confined colonies of the solitary amoeba Acanthamoeba castellanii, in which oxygen consumption continuously reshapes the local environment to generate self-organized guidance cues. Combining quantitative videomicroscopy, oxygen mapping with phosphorescent sensing films, and oxygen-controlled microfluidics, we directly measure both the oxygen landscape and the aerotactic response governing colony expansion. We then develop a Patlak-Keller-Segel (PKS) -type drift-diffusion model whose transport coefficients are entirely determined from independent experiments and microscopic-to-macroscopic modeling approach, without adjustable parameters. The model quantitatively reproduces the observed dynamics, including the coexistence of a fast ballistic front and a trailing transient accumulation band. We show that both diffusion and aerotactic transport are modulated by the same collision-controlled mechanism through density-dependent changes in cell persistence. More generally, our results establish transport coefficients as measurable emergent properties bridging microscopic cell dynamics and macroscopic collective behavior, providing a predictive framework for oxygen-guided migration in living active matter.