PNAS Nexus

Oculocutaneous albinism (OCA) is a genetic condition associated with impaired visual acuity and increased skin cancer risk. When OCA is due to defects in melanosome ion transport, abnormally acidic conditions in the melanosome lumen inhibit tyrosinase, the critical pigment synthetic enzyme. Hence, a therapeutic approach that optimizes melanosome pH to increase pigment production presents a potential treatment for OCA and a method for decreasing skin cancer risk. Here, we report that reduction in sAC (ADCY10) activity via naturally occurring human variants in ADCY10 restores OCA pigmentation, and sAC inhibition increases melanin synthesis in both human and mouse OCA models. These findings demonstrate that targeting melanosome pH is an effective, previously untapped therapeutic strategy for OCA and elevated skin cancer risk.

Biological phase changes provoked by stress, such as vitrification or gel-sol transitions, enable many organisms, including extremotolerant tardigrades, to enter quiescent states and survive extreme environmental conditions. Protein-driven phase transitions are hypothesized to produce large-scale changes in intracellular viscosity, allowing tardigrades to survive extreme stresses such as desiccation. We report that the tardigrade Hypsibius exemplaris undergoes both large-scale and local increases in intracellular viscosity following exposure to anoxic and hyperosmotic stress. Such dramatic shifts in cellular viscosity would be expected to enhance cellular resilience to physical force. Indeed, we found that tardigrades can survive, behave normally, and reproduce after exposure to the highest simulated hypergravity (HG) achievable in an ultracentrifuge (one million times Earths gravity). In contrast, Caenorhabditis elegans, a similarly sized animal, does not survive these extreme forces owing to loss of cellular integrity. Remarkably, tardigrades frozen during exposure to extreme hypergravitational force show minimal disruption of fine cellular ultrastructure and little evidence of stratification of cellular components whose density varies by nearly a factor of two. Further, exposure to anoxia, hyperosmotic stress, and HG all result in a large increase in reactive oxygen species (ROS), which is required for survival under these extreme environments. Inhibition of NADPH oxidase (NOX) suppresses survival both to HG and hyperosmotic stress. Our findings suggest that intracellular viscosity changes in response to multiple extreme stresses may underlie the resilience of these animals to extraordinary physical stress, and that survival in or recovery from these states relies on ROS signaling via NADPH oxidase. Significance StatementTardigrades are renowned for surviving conditions that are lethal to nearly all other life forms. We reveal two mechanisms that support this resilience: intracellular viscosity changes and NADPH oxidase-mediated ROS signaling. Through direct assessment of the effects of altered cellular material properties, found that tardigrades are resilient to forces up to one million times Earths gravity, establishing them as the most hypergravity-resistant animal currently known.

Chronological age is a potent determinant of clinical events, but it is conventionally treated as a linear function of time rather than a dynamic process shaped by genetics and tissue-specific senescence. Deep learning models derived from cardiovascular imaging offer an opportunity to quantify biological age across multiple domains and to examine the extent to which these measures capture shared or distinct vulnerabilities. Here, we applied deep learning to estimate biological age from electrocardiograms, cardiac MRI, carotid ultrasound, and retinal imaging, capturing electrical, structural, macrovascular, and microvascular domains in more than 100,000 UK Biobank participants. Genome-wide association and cross-trait heritability analyses showed that cardiovascular aging is not a singular process but a modular phenotype with distinct genetic determinants across modalities. Polygenic risk scores supported these distinct trajectories, showing that different biological age measures capture partly divergent biological processes with corresponding differences in clinical associations. Modality-specific genes also showcased distinct cell-type enrichment patterns. By deconvoluting aging into electrical, structural, macrovascular, and microvascular components, our results demonstrate that AI-derived age metrics capture distinct, disease-specific aging pathways. Ultimately, this modular framework positions deep learning-derived aging models not as holistic measures of health, but as domain-specific biomarkers of cardiovascular vulnerability.

Cells lying in a curved environment can respond to the surface curvature by reorienting their shape. However, whether cells respond to the mean curvature and/or the Gaussian curvature remains largely unexplored. Here, inspired by experimental observations of how ovarian theca cells (TCs) orient themselves on substrates with different curvatures, we propose a theoretical framework for active nematic layers on curved surfaces. In this model, we assume that the nematic directors of the cells respond to both the mean curvature and the Gaussian curvature of the underlying substrate surface. Our theory predicts specific cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. In addition, by incorporating curvature-induced active traction, our model successfully recapitulates the experimental observation of TC accumulation at convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Overall, our work reveals the unexpected role of cell curvature sensing in driving collective migration and pattern formation on various substrate curvature. SIGNIFICANCESubstrate surface curvature is a critical environmental cue that can influence multicellular organization and functions. Yet how cells collectively align and migrate on complex curved surfaces remains unclear. Here, we proposed a hydrodynamic theory of active nematic layers over curved surfaces for contractile theca cells (TCs), where we assume that the nematic directors of cells can respond to both the mean curvature and the Gaussian curvature of the underlying substrates. Our theory predicts distinct cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. Furthermore, by introducing curvature-induced active traction, our model recapitulates experimentally observed accumulation of TCs at the convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Together, our study provides a simple theoretical framework to unify our understanding of curvature sensing across complex topology, providing insights into geometric control of tissue pattern formation.

Glycosaminoglycans (GAGs) are extracellular matrix polysaccharides whose sequence variability and chemical modifications, particularly sulfation, generate substantial structural diversity. However, how sulfation patterns and monosaccharide composition encode secondary structure in GAGs is not systematically resolved, and quantitative metrics for classifying these structures are largely lacking. Here, we employ large-scale all-atom molecular dynamics simulations to investigate the molecular origin of secondary structure in sulfated GAGs. We systematically vary sulfation patterns and monosaccharide composition to isolate the factors that promote changes in three-dimensional structure. We show that GAG helical conformations arise from recurrent local shortening motifs caused primarily by stabilization of O_SCPLOWLC_SCPLOW-iduronic acid in the 1C4 puckering conformation, promoted by 2-O-sulfation or by densely sulfated regions. We also introduce a two-parameter structural metric that objectively classifies GAG secondary structures and distinguishes heparin helices from related conformations. Together, our results establish a quantitative link between monosaccharide identity, sulfation pattern, and three-dimensional organization of polysaccharide chains, providing a framework for future studies of sequence-structure relationships in GAGs.

Accurate forecasting of seasonal influenza is critical for public health preparedness, and data-driven models are central to this effort. However, most approaches rely on aggregate indicators of influenza-like-illness (ILI), which can obscure heterogeneity and limit predictability at longer horizons. While subtype dynamics are well established, their role in data-driven forecasting remains incompletely understood. Here, we integrate subtype-resolved surveillance data into diverse data-driven frameworks using over a decade of U.S. surveillance records to evaluate and decompose predictive signal in influenza forecasting. Across pre- and post-COVID-19 periods, subtype-informed models consistently improve over baseline models trained on aggregate ILI alone, with the largest gains at longer horizons. Decomposition reveals a horizon-dependent reorganization of predictability: autoregressive persistence in recent aggregate incidence dominates at short horizons but declines with lead time, while predictive signal shifts toward subtype-derived structure. Within this structure, interaction-related features among co-circulating subtypes grow systematically with forecast horizon, indicating that longer-term predictability is driven increasingly by interaction structure rather than marginal subtype composition alone. Together, our results show that subtype information provides non-redundant predictive signal and extends the effective forecasting window of data-driven models. More broadly, our findings suggest that aggregation of heterogeneous subtype processes can obscure latent predictability, supporting subtype-resolved surveillance.

Snow algae darken snowpacks and accelerate melt worldwide. Although elevation strongly structures the physical conditions of mountain snowfields, its influence on snow algal traits and their effects on snowpack reflectance remains unclear. Here, we investigated snow algal composition, cellular traits, and optical properties in summer blooms across an elevational range of 1,059-3,423 m a.s.l. in the western United States, spanning two elevational gradients in the Cascade Range (CA, OR, WA) and the Rocky Mountains (UT, WY, MT). Across all samples (n = 294), snow albedo declined strongly with increasing algal cell density, indicating that total biomass, rather than pigment composition, is the dominant driver of albedo reduction. However, within Sanguina-dominated blooms (117 of 206 samples bloom samples identified across the dataset), neither relative abundance nor algal cell density varied systematically with elevation. Instead, mean cell size increased with elevation, while per-cell pigment concentrations declined, leading to higher astaxanthin:chlorophyll-a ratios driven primarily by reductions in chlorophyll-a per cell. These elevation-dependent shifts in cell size and pigment balance were consistent across both mountain ranges, indicating phenotypic acclimation to increasing environmental stress with elevation. Together, these findings link cellular-scale acclimation of a widespread snow alga to radiative processes shaping mountain snowpacks.

Background. On 8 May 2023 the Japanese Ministry of Health, Labour and Welfare reclassified COVID-19 under the Infectious Disease Control Law from a designated infectious disease (with case-by-case reporting requirements comparable to those of a Category-2 disease) to a Category-5 ("Class-5") notifiable disease, joining the same category as seasonal influenza and most other endemic respiratory infections. Under this regime, COVID-19 case counts are reported weekly from a nationwide network of sentinel medical facilities (initially approximately 5,000, reduced to approximately 3,000 following an April 2025 surveillance reform), and individual case reporting is no longer required. We aimed to characterize the spatial topology of COVID-19 epidemics under this sentinel-surveillance regime and to detect, in a data-driven manner, any structural change in epidemic dynamics over this period. Methods. We analyzed weekly per-sentinel-facility COVID-19 case counts in all 47 prefectures of Japan from 2023-W17 to 2026-W19 (159 weeks). For each week we computed the Shannon pseudo-entropy S of the prefecture-share distribution and global, local, and time-lagged Moran's I across a 92-edge contiguity-based adjacency matrix. To identify any structural change in a data-driven manner, we adopted a two-stage approach motivated by an empirical regularity established in Section 3: we first verified the wave-amplitude-invariant entropy ceiling (S_max >= 3.80 in all five pre-transition waves), then restricted change-point detection to the weeks after S(t) last attained this ceiling, applying PELT, CUSUM, and Bai-Perron sup-F within this restricted region. Seasonal structure was characterized by truncated Fourier regression with first-order autoregressive errors (Cochrane-Orcutt) over harmonic orders K = 1 to 6; between-period comparisons used moving block bootstrap as the principal inferential statistic. Results. The five epidemic waves during 2023-2025 followed a stereotyped spatial template in which S(t) traced a characteristic U-shape around each peak, with a wave-amplitude-invariant entropy ceiling reaching on average 99.4% of the theoretical maximum ln 47 (range 3.820-3.836, SD 0.006). The last week in which S(t) attained this entropy ceiling was 2025-W42. Restricting change-point detection to the 29 subsequent weeks, PELT and CUSUM localised the structural break to late 2025: PELT identified 2025-W48 (robust across penalty values >= sigma^2*ln(n) and across entropy-ceiling thresholds 3.78-3.82) and CUSUM peaked at 2025-W50 (p < 0.0001), placing the break within a two-week window centred on late November 2025. Bai-Perron sup-F peaked later at 2026-W02 (p = 0.062, with reduced power on n = 29). We adopted 2025-W48 as the principal change-point, defining 135 pre-transition weeks and 24 post-transition weeks. Two anti-phase spatial modes were identified in the pre-transition record: a summer-onset Okinawa-seeded Kyushu cascade (Mode A; annual peak epi week 26) and a winter-onset Tohoku-centred connected-cluster mode (Mode B; annual peak epi week 51), approximately 25 epi weeks out of phase. After the regime transition, this ceiling was not attained, and the spatial-persistence ratio I(tau = 8 wk)/I(0) shifted from a highly variable distribution centred near 0.27 (pre-transition, 125 weeks) to a tightly clustered distribution around 0.89 (post-transition, 24 weeks); the mean difference was 0.62 (95% bootstrap CI 0.32 to 0.90; moving block bootstrap p < 0.0001 across block lengths 1-12). The principal finding remained significant under autoregressive-augmented null models and was robust to adjacency-matrix choice, the April 2025 surveillance reform, harmonic order K = 1 to 6, and Okinawa exclusion. Conclusions. Data-driven analysis of 159 weeks of Japanese sentinel surveillance identifies a candidate spatial-persistence regime transition emerging in late November 2025, in which the spatial structure of weekly case shares persists for at least 8 weeks rather than dissipating as in pre-transition. The transition coincides with loss of the wave-amplitude-invariant entropy ceiling and with absence of the Mode A signature through the observed post-transition period. The recent uptick in Okinawa case shares (continuing through 2026-W19) leaves open whether the Mode A signature is structurally suppressed or merely deferred; observation through summer 2026 is required to distinguish a sustained shift from a transient anomaly.

Network geometry offers a principled lens for understanding the structure of biomedical knowledge. We apply exact Ollivier-- Ricci curvature (ORC) -- a discrete analogue of Riemannian curvature computed via optimal transport -- to medical ontologies, disease comorbidity networks, biological interaction networks, and brain functional connectivity graphs. Three main results emerge. First, within a single database (the Human Phenotype Ontology), the formal IS-A taxonomy is hyperbolic ([Formula], tree-like), while the disease co-occurrence network is spherical ([Formula], clique-rich) -- a six-order-of-magnitude gap in the density parameter that the curvature phase transition framework predicts without free parameters. Second, age-stratified disease comorbidity networks from 8.9 million Austrian hospital patients reveal a geometric aging trajectory: mean ORC increases monotonically from [Formula] (age 20-30) to [Formula] (age 80+), driven by rising clustering and density that encode the accumulation of multimorbidity. Third, sedenion ([R]16) Mandel-brot orbit features -- exploiting the zero-divisor structure of the Cayley-Dickson tower -- discriminate ASD-like from ADHD-like brain network topology (AUROC = 0.990, sedenion-only), providing complementary geometric information to ORC. Canonical biological networks (C. elegans neural, E. coli gene regulatory, protein-protein interaction) are uniformly spherical, suggesting that evolved biological networks universally favour redundant, triangle-rich connectivity. All core mathematical claims are machine-verified in Lean 4 (0 sorry in 7 core modules). These results establish ORC as a quantitative geometric biomarker for biomedical network analysis and demonstrate that the same phase transition framework governing semantic networks extends to clinical and biological domains.

The mechanical origin of cerebrospinal fluid (CSF) and interstitial fluid (ISF) transport remains unresolved. High-frequency arterial pulsations ([~] 1 Hz) have long been proposed as a driver of CSF flow, yet multiple biomechanical analyses suggest that their ability to support deep bulk interstitial transport is severely limited by the poroelastic resistance of neural tissue. At the same time, slow-wave sleep is associated with large, synchronous CSF oscillations and enhanced clearance-related dynamics near [~] 0.05 Hz. What selects this low-frequency regime remains unclear. Here we propose a theoretical framework in which this frequency selection is not incidental, but mechanically necessary. When neural populations update their state, local thermodynamic demand induces microvascular dilation. Under intracranial volume constraints, this blood-volume expansion must, to leading order, be compensated by displacement of other intracranial volume components, including CSF. We model the poroelastic response of the interstitial matrix and obtain an effective low-pass filter for this displacement, with a nominal cut-off frequency in the slow-wave range (rc {approx} 0.05 Hz). This mechanical filter implies two distinct forcing regimes. During wakefulness, rapid commitment and sensorimotor resetting are hypothesized to generate spectrally sharp, high-frequency transients in vascular volume. Because this spectral content lies largely above the poroelastic passband, waking dynamics are predicted to be inefficient at driving deep bulk transport. Slow-wave sleep, by contrast, reduces rapid commitment-like transitions and permits smoother, more globally synchronized vascular-volume oscillations that fall within the passband and support larger-scale CSF motion. The framework yields several falsifiable predictions, including load-dependent modulation of sleep-associated CSF pulsation amplitudes, a BOLD-first / CSF-second temporal ordering during slow-wave events, and a mechanical discrepancy between deep interstitial transport and the rapid dispersion of superficial exogenous tracers. More generally, the theory advances a strong claim: the sleeping brain is mechanically privileged for large-scale CSF dynamics not because sleep introduces a new driver, but because sleep permits forcing in a frequency range that brain tissue can actually transmit.

The emergence of amino acids (AAs) and nucleobases (NBs) across meteorites, interstellar ices, and laboratory shock experiments presents a paradox: why do these specific molecular motifs--a minuscule subset of organic chemistrys combinatorial space--appear repeatedly across diverse environments, in the absence of biological selection? We identify a physical mechanism, prebiotic selection, which biases driven chemical systems toward configurations with high stationary probability p*(x) under sustained entropy flux. The bias is quantified by an information quasi-potential {Phi}I (x) = - ln p*(x), entering the overdamped Langevin dynamics O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD where {Sigma} is the local entropy production rate (Schnakenberg 1976). {Phi}I is defined self-consistently via the full non-equilibrium stationary density, avoiding the circularity of identifying it with a scalar potential. Two central theorems underlie the framework. Theorem 1 establishes that {nabla}{Sigma} and {nabla}{Phi}I are generically linearly independent off equilibrium, so the dynamics is genuinely two-field. Theorem 2 (structural constraints on single-field gradient dynamics) shows that single-field models on compact manifolds (i) produce yield curves that are at most unimodal under linear driving, and (ii) combine disjoint perturbations additively, giving superlinearity factor S = 1 + O(||{delta} V ||2). The observed superlinear synergy of Ferris et al. (1996) lies far outside this perturbative bound and therefore requires the two-field structure of EOM-IFF; the non-monotonic peak of Blank et al. (2001) is consistent with two-field dynamics and also with single-field dynamics in the unimodal-with-peak case of Theorem 2 part 1, so it does not by itself discriminate. From these results, we: (i) define a formal substrate-minimal criterion for prebiotic selection; (ii) show consistency with the non-monotonic shock-synthesis yield of Blank et al. (2001) (R2 = 0.885, peak at P* = 28.4 {+/-} 1.4 GPa); (iii) show consistency with the superlinear clay-catalysed RNA polymerisation of Ferris et al. (1996) (synergy factor S {approx} 5.75, robust under {+/-}1-nucleotide measurement uncertainty); and (iv) state two further falsifiable predictions awaiting dedicated experimental tests. Every lemma and theorem is accompanied by explicit assumptions, regime of validity, and regime of failure; the frameworks scope is what it claims, not more. Prebiotic selection is identified as a physical process distinct from and prior to biological selection, offering a unified account of chemical convergence in carbon-nitrogen chemistry under sustained entropy flux.

Collective migration and pulsatile flows in epithelial monolayers are commonly quantified using projected area, implicitly assuming constant cell volume and prism-like cell geometry. These "21/2D" assumptions neglect the intrinsic three-dimensional height and volume dynamics that accompany density fluctuations in confluent, space-filling tissues. Here, we combine 2D quantitative phase imaging (QPI) and 3D refractive index tomography to obtain time-lapse maps of height, volume, and dry mass in Madin-Darby canine kidney (MDCK) epithelial monolayers undergoing collective motion. This is, to our knowledge, the first systematic use of QPI to quantify epithelial monolayer height, volume, and mass dynamics in situ. From independent measurements of refractive index and height, we determine an average dry mass concentration cd = 0.287 g/ml with 2% variability between cells and over time, demonstrating tight regulation of dry-mass density even during large-amplitude pulsations and density changes. The mean height of the monolayer increases with cell density, while the mean cell volume decreases, revealing contact inhibition of cell size. Pixel- and disc-wise statistics show broad, gamma-like height distributions and strong spatio-temporal height fluctuations that remain substantial at high cell density. Cell-resolved tracking demonstrates that height, area, and volume fluctuate synchronously, with volume changes dominated by area rather than height variations, while dry-mass density remains nearly constant. Dynamic structure-factor analysis reveals subdiffusive dynamics and propagating compression-decompression waves, and a continuum mass-flux analysis shows that the depth-averaged continuity equation fails on cellular scales and is restored only after spatial and temporal coarse-graining. Using simple geometrical models, we show that prismatoid cell shapes with constant true volume can reproduce the observed correlations between height, apical area, and "projected" volume, implying that non-prismatic cell geometry biases 21/2D estimates. Together, these results overturn the assumptions of mass/volume conservation and plug-flow-like monolayer kinematics at cellular scales, and highlight the need to incorporate dry-mass regulation and 3D cell shape into models of epithelial dynamics. SIGNIFICANCE STATEMENTUsing QPI, we provide the first comprehensive and time-resolved characterisation of epithelial monolayer height, volume, and dry mass in situ, yielding quantitative measures that both extend and revise earlier work based on 2D imaging alone. Our measurements challenge two long-standing assumptions in epithelial physics: that cell mass or volume is conserved on the timescales of collective motion, and that monolayers behave as "21/2D" plug-flow sheets with vertical, prism-like cells of equal apical and basal area. These findings necessitate a re-examination of prior experimental interpretations and a reassessment of when existing continuum and cell-based models faithfully describe epithelial monolayer dynamics. They also provide benchmarks for future 3D theories and experiments.

The spread of successive novel COVID-19 variants presented a challenge for outbreak surveillance, epidemiology, and emergency responses. Monitoring the emergence and spread of SARS-CoV-2 variants is essential to allocate limited public health resources and optimize control efforts. Global collaboration among the scientific community enabled large-scale viral surveillance and sequencing efforts. However, translating these vast datasets into actionable public health inferences requires rapid statistical methodologies, scalable workflows, and robust frameworks. In this study, we focused on the Delta epidemic wave in Georgia by applying a hybrid maximum likelihood (ML) and Bayesian phylodynamic approach. We characterized the Delta variant introduction to Georgia and its subsequent local spread. Our analysis of 9,783 Delta sequences collected between August 1, 2020 and January 25, 2022 detected at least 344 introductions into Georgia, resulting in 34 highly-supported local clusters. On average, clusters circulated for one month before the earliest detected sequence, highlighting critical delays in detection. While most clusters remained small, a few introduction events led to large, sustained outbreaks. We jointly inferred the statewide transmission network, estimated from all locally circulating clusters with a modified Bayesian discrete trait phylogeographic reconstruction of statewide health districts. We showed that South Central, Georgia was a major source of transmission, despite having smaller numbers of infected people, compared to major metropolitan areas. Our study addresses the urgent need for methodologies and data-driven recommendations for public health practice, particularly given large, dynamic, and integrated datasets. By identifying key geographic sources and sinks of transmission, our findings can guide resource allocation and prepare for future epidemics among high-risk populations. Additionally, by characterizing introduction events, local circulation, and detection lags, we highlight critical gaps in surveillance. These gaps can inform outbreak investigation and response, such as targeted contact tracing and testing.

Transmissible hospital-acquired infections (HAIs) arise from complex, time-varying interactions among patients, healthcare workers, and clinical environments. Although data-driven approaches like graph neural networks (GNNs) effectively model these contacts, they often function as black boxes that over-look established epidemiological principles, limiting interpretability and clinical trust. Inspired by physics-informed neural networks, we propose a epidemiology-informed GNN (EIGNN) framework for patient-level state transitions prediction in dynamic hospital settings, integrating mechanistic epidemiological models into GNNs in a principled manner. Patient-level risk factors learned from dynamic contact networks are jointly leveraged to infer latent epidemiological states, predict state transitions across multiple horizons, and estimate key epidemiological parameters, including transmission and recovery rates. We evaluate the approach on a real-world hospital-onset COVID-19 cohort and two public datasets simulating viral and bacterial HAIs. Across multiple architectures and horizons, EIGNNs achieves AUC-ROC up to 98.46% while providing interpretable, mechanistically consistent insights, offering a transparent tool for infection prevention and control.

The sodium-potassium ATPase (NKA) consumes 19-28% of cellular ATP and is critical for maintaining ion homeostasis. Understanding its energetic efficiency is essential for comprehending cellular physiology and pathophysiology. We develop bond graph models of the NKA that ensure thermodynamic consistency by enforcing conservation of mass, charge, and energy. A simplified 6-state model captures biophysics comparable to a 15-state model while remaining computationally tractable. Through detailed energetic analysis, we demonstrate that under physiological conditions, approximately 65% of the energy from ATP hydrolysis is stored as chemical energy in ion gradients, 10% as electrical energy in the membrane potential, and 25% is dissipated as heat, yielding an overall efficiency of [~]75%. We investigate how the free energy of ATP hydrolysis ({Delta}GATP), intracellular Na+, and extracellular K+ affect NKA efficiency and activity. A critical threshold exists at {Delta}GATP {approx} - 48 kJ/mol below which chemoelectrical transduction drops dramatically, consistent with NKA inhibition under ischemic conditions. The bond graph framework enables quantitative comparison of different NKA models and provides a systematic approach for analyzing ion pumps. SIGNIFICANCEThe sodium-potassium ATPase is one of the bodys most energy-consuming enzymes, yet its energetic efficiency and mechanisms remain incompletely understood. This study presents the first comprehensive energetic analysis using bond graph modeling, guaranteeing thermodynamic consistency. By demonstrating that simplified 6-state models capture essential energetic behaviors of complex 15-state models, we establish bond graphs as a powerful, tractable tool for energetic analysis, model comparison, model selection and validation. The bond graph approach can be applied to other transporters, offering a powerful tool for systems physiology and drug discovery.

Respiratory viruses spread within the host through both local expansion and occasional long-range dissemination that seeds new infection foci. We present LEAP, an analytically tractable within-host model that captures this two-scale process by coupling local plaque growth to long-range seeding. The model reduces to an age-dependent branching process and yields a closed-form expression for the exponential growth rate during early infection. Using empirical data to parametrize the model, we find that productive dissemination requires only a small number of successful long-range seeding events per infected cell, with distinct values for SARS-CoV-2 and influenza A virus. LEAP further predicts that, in these well-adapted viruses, interferon-mediated restriction only weakly affects exponential growth, while remaining decisive for poorly adapted ones. More broadly, the model provides a flexible framework for experimentally testable predictions of early infection dynamics. Significance StatementRespiratory viral infection is an inherently spatial process, in which the virus must colonize large areas of the airways to optimize reproduction. Recent studies in animal models infected with influenza A or SARS-CoV-2 have documented long-range stochastic jumps of viral populations between distant regions of the respiratory tract. The emerging picture is one of two co-occurring spatial processes: slow, local plaque expansion and long-range seeding events that are rare but crucial to rapid colonization of the airways. We introduce LEAP (Lotka-Euler Airway Pathogen model), a simple mathematical within-host model that captures these two-scale dynamics by coupling local plaque growth to stochastic long-range seeding. Using LEAP, measurements from the petri dish can directly produce predictions of infection dynamics in the body.

Mainland China experienced multiple waves of COVID-19 pandemic during 2020-2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible-Vaccinated-Exposed-Infectious-Recovered-Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized pre-delta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing cluster-size heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70-30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

Introduction: COVID-19 transmission has not been evenly distributed across racial groups, with exposure being shaped by social and structural factors. The emergence of highly transmissible variants (i.e., Omicron) dramatically increased infection rates. However, it remains unclear whether racial disparities in transmission disappeared or persisted over the course of the pandemic. Objective: To understand how SARS-CoV-2 transmission differed by race in Canada and whether those disparities changed with the Omicron variant. Methods: We analyzed cross-sectional SARS-CoV-2 seroprevalence data from the Canadian Blood Services serosurveillance program (June 2020 to April 2023) using a previously described dynamic susceptible-infection model, while accounting for seroreversion. Race-specific force of infection was estimated for the pre-Omicron and Omicron periods (with the emergence of Omicron defined as beginning December 26, 2021). Results: Prior to Omicron, racialized individuals had a 74% higher force of infection (IRR = 2.205; 95% CI: 2.115-2.299). During the Omicron period, infection rates rose significantly within each racial group relative to the pre-Omicron period, with a 55.52-fold increase among White individuals and a 31.27-fold increase among racialized individuals. Despite this, racialized individuals remained disproportionately affected following the emergence of Omicron, with 24% higher infection rates than those of their White counterparts (IRR = 1.242; 95% CI: 1.231-1.253). Conclusion: Widespread transmission during Omicron did not result in epidemiologic equity, as racialized populations continued to experience higher infection risk despite crude seroprevalence depicting convergence.

Cardiac arrhythmias are abnormal heart rhythms characterized by disordered electrical dynamics that impair cardiac function and pose a major global burden of morbidity and mortality. Early and accurate prediction of arrhythmic anomalies from physiological time series is crucial for effective intervention, yet remains challenging due to the nonlinear, nonstationary, and individualized nature of cardiac dynamics. Despite significant advances in machine learning-based arrhythmia detection, most existing methods operate as static classifiers on electrocardiographic signals and lack online prediction, patient-specific adaptation, and mechanistic interpretability. From a dynamical-systems perspective, arrhythmias represent qualitative regime transitions, often preceded by subtle, temporally extended deviations that are difficult to detect in real time. Here we introduce CASCADE (Chaotic Attractor Sensitivity for Cardiac Anomaly Detection), an online and personalized anomaly forecasting framework built on a special type of reservoir computing called Dynamical Systems Machine Learning (DynML). DynML employs ensembles of continuous-time nonlinear dynamical systems as chaotic reservoirs to reconstruct and forecast short-term cardiac dynamics on a beat-to-beat basis, training only a linear readout. This design enables efficient online adaptation without retraining the underlying dynamical model. Rather than relying on static beat-level classification, CASCADE identifies arrhythmic events as failures of short-term predictability, manifested as statistically significant deviations between predicted and observed dynamics relative to subject-specific baselines. Detection performance is governed by the intrinsic dynamical complexity of the reservoir, quantified by topological entropy. Reservoirs operating near critical entropy regimes optimally amplify subtle, temporally extended irregularities in heartbeat dynamics, rendering incipient arrhythmic signatures linearly separable at the readout level. Topological entropy thus serves both as a predictor of model performance and a principled control parameter for reservoir design. When evaluated on the MIT-BIH Arrhythmia dataset, CASCADE achieved consistently high F1 scores, precision, recall, and overall accuracy across diverse patient populations, demonstrating strong generalizability across clinical and real-world settings. By integrating chaotic reservoir computing, entropy-guided tuning, and online personalized forecasting, CASCADE reframes arrhythmia detection as a problem of dynamical regime transition rather than static classification. This perspective provides a scalable, interpretable, and computationally efficient framework for real-time cardiac monitoring and early-warning clinical decision support.

The number of offspring produced per reproductive cycle varies widely across animals and is constrained by the number of ovarian follicles that proceed to ovulation. In vertebrates, this phenomenon has been explained by a luteinizing hormone receptor (LHR)-threshold model, in which only follicles expressing sufficient levels of LHR respond to the LH surge and proceed to ovulation. Here we propose a novel mechanism that explains the difference between ovulatory (F1) and non-ovulatory (F2) follicles using the cloudy catshark as a model. The cloudy catshark possesses a hierarchical ovary and produces only two eggs per reproductive cycle. Both F1 and F2 follicles are capable of receiving and responding to LH, as evidenced by their comparable expression of lhr and the downregulation of lhr following LH surge. Nevertheless, LH stimulation selectively activates transcriptional programs associated with the ovulatory process exclusively in F1 follicles. These include progesterone production via star2 upregulation, as well as cancer-associated transcriptional pathways, including transcription factors runxs, prostaglandin-related genes (ptgs2 and ptger1), and matrix metalloproteinases. These results indicate that ovulatory and non-ovulatory follicles may exhibit qualitatively distinct transcriptional responses to the LH surge, potentially challenging the prevailing LHR-threshold model in vertebrates, in which LHR expression is considered a key determinant of ovulatory competence.