PNAS Nexus

Non-structural carbon compounds (NSCs) serve to buffer short-term imbalances between carbon supply and demand in trees; however, their seasonal dynamics throughout the entire tree remain inadequately understood. We quantified year-round non-structural carbohydrate storage and fluxes in a temperate pine forest by integrating monthly measurements of soluble sugars, starch, and lipids across five tissues with biometric scaling to ecosystem stocks. Soluble sugars were consistently highest in canopy tissues and maintained a relatively stable concentration, even as sugar fluxes exhibited pronounced seasonal variations and reversals. In contrast, starch showed clear seasonality, increasing during the mid-growing season and decreasing later, whereas lipid pools remained relatively stable and contributed minimally to short-term fluctuations. Ecosystem-scale analyses indicated that sugars predominantly contributed to NSC turnover, accounting for approximately 80% of the total annual flux, while stored pools exhibited slower changes. The net annual NSC flux, approximately 65 g C m-2 yr-1, was relatively modest in comparison to biomass production, which totaled around 522g C m-2 yr -1. These findings indicate that seasonal changes in carbon balance are primarily driven by rapid redistribution of soluble carbon rather than by significant changes in overall NSC storage.

Understanding the cellular and physiological mechanisms underlying muscle remodeling requires model systems that allow rapid, reliable, and quantitative assessment of muscle state. The cricket Gryllus lineaticeps naturally undergoes non-pathological striated muscle breakdown (histolysis), making it a promising system for studying this process. However, current assessments of muscle state are largely qualitative, subjective, and poorly standardized across experiments. Here, we developed and validated a continuous, quantitative muscle color metric to objectively capture histolysis progression and functional changes in muscle. We show that this metric robustly tracks variation in muscle color across remodeling stages, including the challenging fully transparent stage, and strongly predicts protein content, mitochondrial abundance, and iron content in a muscle- and trait-specific manner. The reproducibility of these relationships across independent datasets demonstrates the generality and robustness of this approach. By providing a rapid, objective, and biologically informative proxy of muscle state, this framework not only advances the utility of G. lineaticeps as a model for muscle remodeling but also offers a strategy for exploring the cellular dynamics underlying age-related muscle diseases and disorders, addressing an increasing public health concern in aging populations.

Glyphosate, the active ingredient in herbicide Roundup, is the most widely used environmental contaminant that has been extensively studied for its potential carcinogenic effects. In the US alone, a staggering 81% of the US population [≥]6 years of age is exposed to glyphosate. Notably, this coincides with the alarming rise in the incidence of hypertension, the single largest risk factor for global mortality through cardiovascular diseases. Here we asked if there is a link between glyphosate exposure and hypertension, the premise being that glyphosate targets the shikimate pathway present in gut microbiota coupled with more recent knowledge that gut microbiota causally regulate blood pressure. We hypothesized that glyphosate elevates hypertension through microbiota-mediated mechanisms and document a highly concerning detrimental effect of glyphosate by demonstrating a causal link between oral glyphosate exposure and significant elevation in blood pressure. Gut microbiota was identified as the central mediator of this effect. Mechanistically, glyphosate-mediated elevation in blood pressure was through disruption of both gut-liver and gut-vascular homeostasis via FXR-signaling and accumulation of the microbial metabolite shikimic acid, respectively. Together, these findings underscore the need to reconsider the unabated use of this herbicide, which adversely affects a cardinal sign of health.

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.

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.

The temporal stability and spatial heterogeneity of global marine ecosystems under changing climates reveal how biodiversity persists or collapses. However, the deep-time evolution of these phenomena remains poorly understood. We reconstructed the stability landscape of pelagic plankton from the Cretaceous-Paleogene extinction to the present. We find that the Cenozoic was a period of punctuated volatility with high turnover post-extinction and during the Late Neogene cooling. Spatially resolved analysis revealed latitude-dependent trends: equatorial regions stabilized over time, whereas polar communities, especially in the Southern Ocean, destabilized. Equatorial regions homogenized from initially high heterogeneity, whereas polar communities showed the opposite pattern, including a latitudinal seesaw of spatial heterogeneity over the past 30 million years. These findings illuminate temporal stability and spatial heterogeneity dynamics across geologic timescales.

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.

Insulin-producing {beta}-cells demonstrate remarkable heterogeneity in their individual responsiveness to glucose, and that cellular heterogeneity contributes to coordinating islet activity and glucose homeostasis. Our current understanding of how variation in cell-intrinsic factors control cellular excitability and insulin secretion is informed by foundational experiments conducted on dispersed single {beta}-cells. Such studies are limited in their ability to link multiple electrical or metabolic properties within a single cell and preclude the ability to relate, post hoc, each parameters contribution to glucose responsiveness. Computational modelling represents a unique and underutilized tool to integrate and investigate the role of natural {beta}-cell heterogeneity in physiologic glucose responses. Herein, we utilize a high-volume single-cell electrophysiology "patch-seq" dataset to define the physiologically relevant sources of variability in human {beta}-cell electrophysiology and model their influence on single-cell glucose responses. Three thousand in silico human {beta}-cells were fitted to physiologically relevant variations in glucokinase activity, K+ current, Na+ current, Ca2+ current, and exocytotic function. Four dominant electrical phenotypes arose at low (2 mM) and high (20 mM) glucose: silent, bursting, spiking, and depolarized. Approximately 50% of uncoupled {beta}-cells remained electrically silent at high glucose. Furthermore, Na+ channel half-inactivation voltage was a major predictor of the silent and spiking phenotypes at each glucose concentration, and of cells that transition from silent to spiking when glucose increased. Indeed, experimentally observed variation in Na+ channel voltage dependence was second only to variation in ATP-sensitive potassium channel conductance in determining {beta}-cell excitability. Our data-driven computational modelling highlights the functional importance of electrical heterogeneity in human {beta}-cell glucose responses, and provides a useful tool for generating testable hypotheses.

Violence against women is sustained not only by individual behavior but also by social norms that legitimize coercion and control. While attitudes justifying intimate-partner violence have been extensively documented in large-scale household surveys, they are rarely analyzed as structured, predictable population-level phenomena. Here, we model the continuous prevalence of violence-justifying attitudes across 70 countries and demographic subgroups using country-resolved supervised machine learning with strict out-of-sample evaluation. Drawing on harmonized estimates derived from the Demographic and Health Surveys, we quantify how much cross-subgroup variation in normative acceptance is explainable from survey structure alone. By comparing full models that incorporate attitudinal scenario framing with demographics-only baselines, we show that high predictability arises from fundamentally different sources across countries: in some contexts, demographic stratification--particularly education--structures normative acceptance, whereas in others, conditional justification narratives dominate. Integrating independent country-level indicators of gender inequality, human development, and democratic quality reveals that violence-justifying norms are most predictable in structurally polarized settings rather than within a single cultural regime. Together, these findings demonstrate that normative acceptance of violence is not uniformly diffuse but can form coherent, structurally embedded patterns. This cross-scale framework provides a quantitative basis for identifying where prevention strategies may benefit most from demographic targeting versus direct challenges to context-specific justifications of violence. Significance statementNormative acceptance of intimate-partner violence is a measurable societal risk factor, yet it is rarely analyzed as a structured population-level phenomenon. Most quantitative studies remain descriptive, and machinelearning analyses using large-scale household surveys typically focus on individual-level classification of victimization or vulnerability. Here, we model the continuous prevalence of violence-justifying attitudes across 70 countries and demographic subgroups using country-resolved supervised regression with rigorous out-of-sample evaluation. By contrasting demographics-only models with those incorporating attitudinal scenario framing, we show that cross-national differences in predictability arise from distinct sources--demographic stratification in some contexts and conditional justification narratives in others. Linking these patterns to independent indicators of gender inequality, human development, and democratic quality reveals that highly structured norms emerge in structurally polarized settings, highlighting where targeted prevention strategies are most likely to be effective.

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.

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.

Mainland China experienced multiple waves of COVID19 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 predelta, 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 clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 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.

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.

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.

Humans are not equally attractive to mosquitoes, leaving some more vulnerable to mosquito-borne illnesses than others. Body odor differences likely allow mosquitoes to discriminate between humans. Using a uniport olfactometer, we measured the attraction of Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquitoes for each of our 119 participants. Ae. aegypti, but not other species tested, were more attracted to male than female participants. Each of our three species ranked our participants differently, favoring a distinct subset of our cohort. For each species, mosquito attraction rates were used to define high and low attraction human odors and bacterial taxa. For example, Ae. aegypti and Cx. quinquefasciatus attraction was associated with the absence of odors like cyclic alcohols and monoterpenes, while Ae. albopictus attraction was associated with the presence of ketones. Each mosquito species exhibited distinct responses to individual humans, emphasizing both unique and shared cues for targeting their hosts.

Helicobacter pylori infections present a persistent global health challenge due to increasing antibiotic resistance and the bacteriums ability to survive in the acidic gastric environment. Existing within-host models of H. pylori infection neglect the gastric pH fluctuation, despite its role in modulating bacterial growth and antibiotic efficacy. To address this gap, we extend a published in-host model by explicitly incorporating gastric pH as a dynamic state variable, influenced by three key physiological processes (i) bacteria urease which neutralizes gastric acid to create a protective niche; (ii) host acid secretion response, which attempts to restore baseline acidity; and (iii) dietary perturbations, which induce temporary pH changes. Equilibrium and stability analysis reveal pH-dependent reproductive thresholds [R]s(H) and [R]r(H) that determine the conditions for bacterial persistence and treatment outcome. Successful eradication requires driving both thresholds below unity. Numerical simulations validate distinct clinical scenarios including complete bacterial clearance, resistant strain dominance, stable bacterial coexistence, and oscillatory persistence. These outcomes emerge from the coupled interplay between antibiotic pressure, immune response, and pH regulation. Our model provides a comprehensive theoretical framework for understanding H. pylori treatment failures and highlights how adjuvant pH-modulation strategies could enhance antibiotic efficacy against resistant infections.

Human sleep-wake cycles arise from the interplay between homeostatic sleep pressure and circadian rhythms, yet the underlying mechanistic basis by which these drives jointly govern state transitions, recovery from sleep loss, and subjective sleepiness remains unclear. Here, we use the extended Phillips-Robinson model incorporating circadian excitation of orexin and locus coeruleus populations, yielding analytically tractable, circadian-modulated thresholds for sleep onset and awakening, and delineating the roles of circadian and homeostatic drives. We show that homeostatic feedback alone generates intrinsic sleep-wake oscillations via a saddle-node on invariant circle bifurcation, while circadian drive reshapes the stability landscape to account for immediate sleep onset and partial first-night recovery after prolonged deprivation, and enables analytic predictions of sleep timing and duration. We further define sleepiness as the distance between the homeostatic state and the active circadian sleep threshold, which robustly predicts subjective sleepiness across various deprivation, restriction, extension, and recovery protocols. Together, these results establish circadian-modulated thresholds as a unifying dynamical principle linking sleep-wake transitions, recovery dynamics, and sleepiness, with implications for circadian misalignment, shift work, and individualized sleep interventions. Author summarySleep and wakefulness arise from the interaction between circadian timing and the gradual accumulation and dissipation of sleep pressure. Although existing computational models have advanced understanding of these processes, most rely on fixed or heuristic rules for switching between sleep and wake states, limiting their ability to explain behavior under sleep deprivation, extension, or restriction. Here, we present a mechanistic description of state switching in which circadian and homeostatic influences act as separable but interacting control dimensions, providing a framework for how circadian modulation shapes state stability, recovery sleep, and subjective sleepiness, without invoking separate mechanisms for normal and perturbed conditions. By recasting sleep regulation in terms of state-dependent dynamical boundaries, our results offer a unified perspective on sleep timing, duration, recovery, and vulnerability to fatigue. This work provides a more physiologically grounded foundation for sleep-wake modeling and a principled basis for understanding circadian misalignment, shift work, jet lag, and other real-world challenges to sleep health.

The early stages of viral infection constitute a race between viral proliferation and interferon (IFN)-mediated defenses. Recent experiments on single-cell viral kinetics have demonstrated a high degree of stochasticity in the timing of viral release, but how this shapes the competition between virus and host remains unclear. We formulate a stochastic spatial model to address the question of how variability in the release of viral progeny and IFN affect the early infection dynamics. The model distinguishes between two types of timing noise: stochasticity in the initiation of release, and variability in the secretion time of individual virions. Our key result is an asymmetry in how noise affects outcomes: For the virus, stochastic initiation accelerates expansion, while for the host, effective containment via IFN benefits from precisely timed responses. For the secreting states, we find that a broader secretion profile (higher variability in particle release times) is always advantageous. In all cases, we find that stochasticity in signal timing plays a huge/central role in the early infections states.

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.