PNAS Nexus

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene-environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

A key scientific challenge is to develop a universal theory of life that integrates our biological knowledge with fundamental logical principles. We propose that a "five nodes" principle may govern the origin of life and consistently exist hierarchically within living systems. In our investigation, we explored autocatalytic chemical reaction networks (CRNs) as potential origins for methanotrophy and anoxygenic phototrophy, aiming to validate the "five nodes" principle in the emergence of autopoietic systems. Our research revealed the emergence of autocatalytic peptides and weakly reversible realizations within the MSA reaction network (composed of CH4, SO42-/SO32-, and NH4+) as well as in the light-Sammox (sulfurous reduction coupled to anaerobic ammonium oxidation)-driven CRN (composed of HCO3-, SO32-, and NH4+) under hydrothermal conditions. Furthermore, we identified the possible emergence of three main interdependent components essential for life within the two reaction networks: membrane compartments, peptide nucleic acids (PNA) backbones, and catalytic capacities for energy release reactions. Our findings suggest that non-equilibrium synergy of five bioessential elements (NESFBE) can facilitate proto-energy and material metabolism, thereby enabling diverse scenarios for lifes origin. Importantly, our discovery indicates linear constraints present in these CRNs that contributed to lifes inception, which is the mathematical foundation of the "five nodes" principle. Linear constraints determine both the emergence and self-disintegration of autopoietic systems. We infer a period five existence based on hierarchical structures found within autopoietic systems, and "period five indicates autopoiesis" could be one of universal theory of life.

AO_SCPLOWBSTRACTC_SCPLOWCellular automata and graph reaction-diffusion systems encode local spatial interactions in different mathematical forms. We develop a cochain-operator calculus for these two settings. Over a finite field Fq, every local rule on a finite neighborhood has a unique reduced polynomial representative. On an oriented line, the coboundary and endpoint maps recover the left and right shifts. Our main theorem shows that these operators, together with linear operations, constant cochains, and the degree-zero cup product, generate every finite-radius polynomial cellular automaton. Explicit formulas for Rules 30, 110, and 22 show how reflection-invariant linear coupling, directed transport, and nonlinear neighbor interactions enter the calculus. On a general graph, d*d is the unweighted combinatorial Laplacian and enters a graph reaction- diffusion recurrence. Over [R], the term - Dd*d with D [≥] 0 admits the usual diffusion interpretation; over Fq, the corresponding expression defines modular coupling without an intrinsic order. In the morphogenetic examples, we therefore distinguish pattern-generating dynamics from finite-state observation and use the Betti numbers of active induced subcomplexes to summarize observed patterns. This yields a common algebraic representation without identifying real-valued diffusion with finite-field dynamics.

Many experimental studies collect longitudinal physiological measurements while assessing irreversible biological outcomes only at a terminal endpoint, leaving the timing of disease progression unobserved. This disconnect between continuously measured covariates and latent biological events limits quantitative analysis of how physiological stress drives tissue degeneration. We address this problem by formulating retinal ganglion cell (RGC) degeneration in experimental glaucoma as a latent time-to-event process driven by longitudinal intraocular pressure (IOP) exposure. Using monthly IOP measurements and terminal RGC counts from the DBA/2J mouse model of glaucoma, we develop both Cox proportional hazards models and a time-dependent extension based on the Andersen-Gill counting-process formulation, allowing progression risk to depend on both contemporaneous IOP and cumulative pressure burden. We further reconstruct model-implied survival curves from the fitted hazard functions, providing a continuous-time representation of latent disease progression under observed and hypothetical IOP trajectories. Across all disease thresholds and both modeling approaches, cumulative IOP burden above 19 mmHg emerged as the dominant predictor of RGC degeneration, whereas peak and contemporaneous IOP contributed little additional predictive information once sustained exposure was taken into account. HDAP2, a mitochondria-targeted neuroprotective peptide, significantly reduced progression hazard after adjustment for longitudinal IOP exposure, supporting a pressure-independent neuroprotective mechanism. Beyond identifying cumulative pressure exposure as the dominant predictor of neurodegeneration in this experimental model, the proposed framework provides a general strategy for relating longitudinal physiological measurements to latent biological progression. By linking exposure histories to model-implied survival trajectories, it enables trajectory-based risk assessment, prediction under hypothetical IOP trajectories, and quantitative evaluation of therapeutic interventions in experimental systems where biological outcomes are observed only at terminal endpoints.

Exposure to fine particulate matter (PM2.5) increases paediatric respiratory morbidity, but its impact on the intensity and economic burden of care in early childhood remains unclear. Here we link nationwide health records for 3,210,381 South Korean children (99,261,945 child-month observations, 2015-2019) and exploit quasi-random variation in PM2.5 induced by thermal inversions to estimate its causal effects on respiratory care and expenditure. A 1-standard-deviation increase in monthly PM2.5 (7.23 micrograms per cubic metre) raises respiratory visits broadly, peaking at age 1 (0.29 additional visits per child-month) and remaining significant through age 6. In contrast, expenditures rise disproportionately among children under 3, increasing by 46.7%, 43.0%, and 35.4% at ages 0, 1, and 2 and becoming indistinguishable from zero beyond age 4. This shift towards higher-intensity emergency care translates into a 32.2% increase in annual respiratory spending among children under 5. Air pollution thus imposes a dual burden, amplifying both the frequency and severity of illness among the youngest infants.

Forest restoration at scale depends on sowing seeds directly onto the land, yet seedling establishment is often poor and inconsistent. Seed enhancement technologies, such as priming, coating, and pelleting, improve germination under water, temperature, and salinity stress by supplying polymers, nutrients, or microorganisms at the seed surface. Being mainly conformal films, they act continuously once wetted and cannot gate when a seed is exposed to the soil. They also enclose no air volume, so the microenvironment they create is inseparable from the surrounding soil and has been inferred from plant performance rather than measured directly. Whether an engineered carrier can instead set both the state and the duration of the post-dispersal microenvironment, and how such an effect can be directly quantified, remains unexplored. Here we show that hygrochastic seedpods hold seeds in a timed, directly measurable microclimate and accelerate seedling emergence. The pods are programmable, responsive conical shells enclosing an air cavity around the seed. Two design solutions were investigated in parallel: wooden seedpods that crack open upon hydration, and poly(vinyl alcohol)/boric acid seedpods that gradually disintegrate after sustained wetting. In both, wax coatings act as a programmable diffusion-limited timer that sets activation from hours to days. The pod cavity permits direct sensing and monitoring, which no coating geometries allow. As directly measured during the initial phase after deployment, seedpod interiors ran 2 - 3 C warmer and drier than the surrounding soil, and these differences persisted after opening. Across indoor and field trials in several tree species, pods accelerated early seedling emergence without penalizing subsequent growth. These results identify artificial hygrochastic seedpods as a transient ecological mediator that regulates both the timing of seed exposure and the microclimate around the seed, offering a strategy for improving forest restoration under variable environmental conditions.

Within a single human lifetime, the spectral environment has been fundamentally reshaped. Broadband daylight, rich in infrared (IR) photons arising from solar and atmospheric physics, has been replaced in the built environment by narrow, engineered spectra that largely exclude long wavelengths. While modern lighting is optimised for vision, the non-visual photobiology of metabolism may depend on spectral components that are now absent. When expressed in photon-energy units, the solar spectrum exhibits a broad maximum near 0.75 eV. This range overlaps with the activation and reorganisation energies governing mitochondrial electron-transfer kinetics. Within a Marcus-type framework, IR photons are therefore positioned to modulate rate-limiting metabolic steps by biasing barrier-crossing probabilities rather than supplying chemical energy. These wavelengths also penetrate deeply into tissue in a scattering-dominated regime, forming a diffuse internal photon field capable of interacting with distributed mitochondrial networks. We propose the term photometabolism: a solar-driven, non-photosynthetic modulation of core metabolic processes. A scaling analysis shows that photon interception in this band varies with body mass in parallel with basal metabolic rate, suggesting that ambient sunlight provides sufficient flux to influence metabolic kinetics across the biosphere. These findings have implications for physiology, ecology and the design of indoor environments whose lighting spectra increasingly diverge from their evolutionary context.

Extracellular matrix (ECM) remodeling and angiogenesis are essential processes underlying endometrial decidualization during embryo implantation. Fibrillar collagens, the major structural components of the ECM, form the architectural framework of the decidua and undergo dynamic reorganization during this process. However, the functional role of type V collagen--a key regulator of collagen fibrillogenesis-- remains undefined. Here, we demonstrate that Col5a1 is highly expressed in decidual stromal and endothelial cells of the mouse uterus. Conditional deletion of Col5a1 leads to progressive uterine hemorrhage beginning at gestation day 8, culminating in complete embryo resorption and pregnancy loss by day 12. Col5a1-deficient decidua exhibits severe structural distortion, marked disorganization of fibrillar collagen, shallow and misdirected embryo invasion, and profound disruption of decidual angiogenesis and vascular network formation. Transcriptomic profiling further reveals distinct gene expression signatures and signaling pathways regulated by COL5A1 in the decidua. Collectively, these findings identify type V collagen as a critical ECM regulator required for maintaining decidual integrity, supporting angiogenesis, and ensuring successful pregnancy.

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.

Population vulnerability to an infectious disease epidemic is commonly summarized by system-level indicators such as the epidemic threshold: a single critical boundary. Yet transmission is heterogeneous across communities, host groups and transmission pathways, and public-health decisions often require identifying which parts of the system become vulnerable, and under which conditions. Here we show that epidemic criticality can itself be fragmented across population structure. Using multitype branching processes, we identify singularities governing the expected size of outbreaks that ultimately become extinct and show that coupling between population strata transforms their individual thresholds into complex-valued critical points. Their real parts locate critical changes along the transmissibility axis, their imaginary parts determine their strength and smearing, and their modes identify the subpopulations involved. In Italy, this framework reveals localized vulnerability to respiratory-pathogen emergence and improves vaccine allocation over importation-based strategies. For measles in Texas, it identifies spatial units more homogeneous in observed outbreak burden than standard administrative or metropolitan partitions. In a One Health model of livestock-associated MRSA, it separates occupational and human--animal transmission pathways. Epidemic vulnerability is therefore organized by a structured critical landscape rather than a single threshold, and resolving this landscape can directly inform public-health risk assessment and intervention.

Inappropriate antibiotic use presents a major global health challenge, particularly in low-resource settings where access to quality care is limited but antibiotics remain relatively unrestricted. This study estimates the causal effect of frontline primary care quality on inappropriate community antibiotic use, combining detailed community-based data from approximately 100 rural villages in rural China with an instrumental variable (IV) approach embedded within a double/debiased machine learning (DML) framework. We linked objective measures of village doctor clinical practice quality, measured through unannounced standardized patient visits, to household-level antibiotic use data collected from the same villages. To identify the causal effect, we constructed multiple candidate instruments from extensive provider characteristics and used an ensemble of machine learning algorithms within a flexible DML-IV framework to approximate an optimal instrument, addressing a many-weak-instruments problem. We found that improving village provider clinical practice quality reduced both antibiotic receipt during healthcare encounters for common diseases and household antibiotic storage for future self-medication. Our findings suggest that strengthening frontline primary care quality can meaningfully reduce inappropriate community antibiotic use without restricting access to essential treatment. More broadly, this study illustrates how causal machine learning can strengthen conventional causal estimation in complex observational settings in global health economics research.

Does neuronal loss simply reduce measured activity, or also change how the surviving network behaves? We separate these effects in a next-generation excitatory-inhibitory neural field by writing the viable population measure as q_a = lambda_a f_a, where lambda_a is viable population mass and f_a is the normalized survivor distribution. Under state-independent thinning with fixed Cauchy heterogeneity, normalization commutes with the Ott-Antonsen/Montbrio-Pazo-Roxin reduction on the specified analytic invariant manifold. The mortality term disappears from conditional transport, but viable mass remains in recurrent coupling: loss can reshape survivor dynamics, not merely scale their contribution to tissue activity. Conversely, for otherwise identical constant homogeneous parameters, viability, pathway integrity and compensation give exactly conjugate conditional deterministic dynamics whenever c_ab lambda_b^(1-nu_ab) is preserved. Identical conditional activity therefore need not imply an identical biological mechanism. Equilibrium and oscillatory bifurcations, finite-population escape, and delayed propagation reveal consequences of these two principles. In particular, matched field simulations show that localized loss can increase whole-sheet firing through recurrent reorganization, while coherent-wave continuation quantifies viability-dependent propagation and phase relaxation. The framework distinguishes neuronal abundance from survivor state and places an exact limit on mechanism inference. Attributing activity changes to neuronal loss therefore requires information beyond conditional neural dynamics, such as tissue-level measurements or independent structural constraints, interpreted through an appropriate observation model.

Accumulation of proteins such as amyloid beta (Abeta), hyperphosphorylated tau and alpha-synuclein within the brain alters neural information processing and causes neurodegeneration(1-3), but how toxic solutes are cleared from the brain remains highly controversial(4,5). Proposed exit routes include efflux across endothelial cells into the blood(6,7), and movement to the pial surface via vasomotion-induced pumping along spaces within arteriolar smooth muscle(8) or via outflow along the perivascular space of ascending venules promoted by water flux through astrocytes (the glymphatic system(9)). From the pial surface of the brain, drainage may continue to dural lymphatics, along the outer sheaths of exiting cranial nerves and across the cribriform plate(10-14). We now report the presence, in mice and humans, of 2 micron diameter conduits that remove fluorescently labelled tau and Abeta from the brain. These conduits form a spatially-organised mesh within the walls of penetrating arterioles and pial arteries, and around the surface of ascending venules and deep cerebral and pial veins. They course through the pial and arachnoid layers to span the CSF space, wrapping the brain and cranial nerves. They are formed of reticular fibroblasts, which label for VE-cadherin(15) and PDGFRalpha(16), the lymphatic markers(17) podoplanin, VEGFR3 and Prox1, and reticular fibroblast extracellular matrix components collagen I and VI(16,18-20). Parenchymal tau drains from the brain at a similar rate via arteriolar conduits and via conduits around venules, arguing against preferential removal by a glymphatic mechanism. In Alzheimer's disease model mice, Abeta is seen traversing these lymph node-like conduits. Modulation of molecular transfer via this route may accelerate or delay cognitive decline, and slowed transfer from arteriolar to pial-arachnoid conduits may initiate cerebral amyloid angiopathy.

Effective mentorship is essential for strengthening the STEMM (Science, Technology, Engineering, Mathematics, and Medicine) workforce, yet empirical evidence on how mentorship networks are structured and linked to career success remains limited. Here, we analyze mentorship networks among recipients of NIH career development (K) awards to characterize network size, mentor roles, and their associations with mentee-reported outcomes, including potential variation by sociodemographic characteristics. We found that K-awardees rely on mentors beyond their primary advisor, who play varying roles beyond being a Research mentor. Different mentor roles led to different types of mentoring outcomes; while Research mentors were associated with research-related outcomes such as Publications and Grants, career- and psychosocial-related mentoring outcomes were more likely to come from other types of mentors, such as Coaches, Connectors, and Sponsors. Larger networks, as well as having Peer and Identity mentors are additively beneficial for researchers who identify as underrepresented in science more than their counterparts. This study provides large-scale evidence on how mentorship network configurations relate to early-career grant success.

In the two-process model of sleep-wake regulation, circadian-modulated thresholds time every sleep onset and awakening, yet they remain free parameters rather than quantities derived from neuronal dynamics. This limitation leaves the framework unable to predict how sleep patterns change when neuromodulatory drive is altered, as in aging and narcolepsy. Here we close that gap by deriving closed-form, circadian-modulated threshold expressions from the Phillips-Robinson model with explicit orexinergic excitation of the wake-promoting population. Within this single threshold geometry, aging and narcolepsy appear as opposite deformations along the orexinergic axis: age-related hyperexcitability of orexin neurons elevates the sleep-onset boundary and creates a fragility regime in which minor nocturnal disturbances trigger premature awakenings, whereas orexin loss depresses the same boundary toward the wake-onset threshold and produces the rapid state fragmentation of narcolepsy. Concurrently, reduced circadian amplitude compresses the inter-threshold corridor, advancing sleep onset and shortening sleep duration. These results convert the classical two-process thresholds from descriptive conveniences into mechanistic organizers of sleep-wake dynamics across healthy aging and orexin deficiency. Author summaryThe classical two-process model of sleep relies on a pair of switching thresholds that have been imposed by hand rather than derived from the neurons that actually stabilize sleep and wakefulness. Here we remove this limitation: starting from a biophysical mean-field model of the sleep- and wake-promoting populations, and adding the orexin system that stabilizes arousal, we derive the sleep-onset and awakening thresholds analytically from the bifurcation geometry of the underlying dynamical system. These closed-form thresholds depend explicitly on circadian phase, homeostatic state, and orexinergic tone, revealing that aging and narcolepsy are opposite deformations of a single threshold corridor along one orexinergic axis. In aging, orexin hyperexcitability raises the sleep-onset barrier past a sharp "arousal fragility boundary," beyond which a minor disturbance triggers irreversible awakening; in narcolepsy, orexin loss collapses the same barrier and fragments sleep while paradoxically preserving total sleep time. We further find, contrary to common assumption, that orexin sustains wakefulness chiefly by raising the barrier to falling asleep rather than by resisting awakening. This work turns phenomenological sleep thresholds into physics-derived organizers of behavior, providing a mechanistic bridge from neuronal circuitry to whole-organism sleep dynamics.

Zero-dimensional chemical master equations, ordinary differential equations, and compartmental population models replace spatial stochastic biological systems by vectors of total counts or densities. This study asks when that projection is exact and whether information retained in spatial correlations can diagnose its practical failure. Exact Markov closure is characterized by an aggregate-rate lumpability condition: for every retained transition, the sum of microscopic transition rates must be constant over all spatial configurations with the same counts. Violations are connected to BBGKY-type correlation hierarchies and to mean-field, pair, and triplet closures. Conditional rate, finite-time predictive, memory, path-space, and correlation Kullback-Leibler risks quantify distinct losses. An exactly solvable two-compartment reaction separates structural non-closure from recovery of a well-mixed law under fast hidden mixing. Copy number and a spatial mixing-interaction ratio connect concentration, volume, diffusion, and reaction parameters to practical screening, including an Escherichia coli-scale example. The same projection logic is evaluated in controlled spatial susceptible-infectious-removed and predator-prey benchmarks. Across mixed and segregated initial conditions and four mobility regimes, pair-correlation risk was strongly associated with the error of the corresponding zero-dimensional ordinary differential equations (Spearman correlations 0.95 and 1.00; pooled 0.99). A nearest-neighbour exchange sensitivity analysis preserved the positive risk-error ranking. These benchmarks do not establish a universal threshold, but support correlation information as a transferable diagnostic for selecting among count, pair, higher-order, and explicit spatial descriptions.

Economic stability and progress in modern technological societies depend on vigorous and independent public funding of science and engineering research. When peer review or funding decisions are perceived as politically directed, scientists, funding agencies, and the public react in coupled and conflicting ways. We describe an evolutionary game-theoretic model to analyze how perceived political interference in science funding affects the interrelated behaviors of scientists, funding agencies, and the public. The model simulates scientists choosing to refuse peer reviews and retaliate, agencies responding by adopting AI-assisted review and altering reviewer pay, and the public accepting or rejecting these AI systems. Through numerical simulations, five principal findings are identified: (1) Operational capacity and institutional legitimacy are governed by separate conditions and can fail independently. (2) Legitimacy of the process is bistable, meaning final states are determined by the publics acceptance of AI. (3) Since the career cost for researchers refusing to review is generally low, resistance/retaliation cascades can readily ignite, leading identical institutions to entirely opposite fates. (4) Increasing reviewer pay only stabilizes participation within a strict budget-solvency frontier, and emergency pay can paradoxically erode the legitimacy it aims to protect. (5) Finally, finite-population simulations reveal that baseline scenarios partition into either legitimacy recovery without capacity or joint failure, confirming that the fundamental separation of capacity and legitimacy outcomes is a dominant structural feature driven primarily by initial scientific resistance and politicization levels. This theoretical work quantifies issues for future work in science policy. SignificanceModern industrial nations rely on public funding of science, certified through expert peer reviews whose authority rest as much on perceived institutional independence as on processing capacity. We model what happens when review or funding decisions are seen as politically directed, and artificial intelligence (AI) substitutes for resistant human reviewers. Treating scientists, the public, and an adaptive funding agency as interacting populations, the model shows that operational capacity and institutional legitimacy obey separate dynamics and can fail independently: automation can sustain a review pipeline while its authority collapses, or legitimacy can recover while backlogs persist. Under finite populations, identical institutions can reach markedly different fates by chance alone, and how often chance favors survival depends on the noise process assumed, not on a fixed institutional probability. This framework identifies which conditions govern whether resistance ignites or whether legitimacy, once threatened, is repaired, as explicit future priorities for empirical calibration and policy design. Lay AbstractModern societies depend on public funding for scientific research, a system that only functions if the public believes the process is fair. Using evolutionary game theory, this mathematical work explores what happens when scientific peer review is viewed as politically compromised, and funding agencies turn to artificial intelligence (AI) to replace human experts who protest the system. The mathematical model reveals several critical warnings for policymakers: O_LIAutomation Does Not Equal Trust: Keeping grant money flowing and maintaining public trust are two separate problems. An agency can successfully use AI to process grants while the public completely loses faith in its decisions, creating a "zombie" institution. C_LIO_LIThe Threat of "Proposal Flooding": When scientists protest, in principle they refuse to undertake peer review, and can overwhelm the funding agency by submitting many proposals. This retaliatory flooding and refusal to review, within a limited budget, can outpace any AI systems ability to keep up, causing the entire pipeline to collapse. C_LIO_LIEmergency Pay Can Backfire: Trying to secure scientists cooperation by raising reviewer pay during a highly politicized crisis can make things worse, as the public may view this emergency pay as a "bribe," which rapidly destroys whatever institutional trust remains. C_LIO_LIEarly Action is Critical: Human behavior is unpredictable, small early differences in how people react can push identical institutions toward completely different fates. This model tracks individual scientists and members of the public to capture their behavior, which appears to be model-dependent, thus producing sensitivity to early demographic fluctuations and chance factors. Under the standard assumption, every simulated agency ended up with an unmanageable backlog; the only thing chance decided was whether public trust recovered. Under a coarser and more commonly used shortcut, roughly half the agencies appeared to recover fully, and a "zombie" agency that kept processing grants while trust collapsed appeared in about one in six. Policymakers therefore must urgently focus on building trust through transparent, explainable AI rollouts before public rejection becomes permanent. C_LI

Skin growth and remodeling underlies health, disease, and treatments such as tissue expansion (TE). The mechanotransduction pathways in dermal fibroblasts are increasingly well characterized, and tissue-level growth has been described phenomenologically, but coupling between cell-level signaling and tissue-level growth remains poorly understood. We develop a dermal fibroblast signaling network through extensive literature data curation, comprising 151 reactions among 96 nodes. The inputs are mechanical stretch and eight ligands (TGF{beta}, PDGF, FGF, IL1, IL6, TNF, AngII, ET1); outputs of interest span ECM-enzymes (proMMP1/2/9, MMP1/2/9), ECM proteins (CImRNA, collagen I, fibronectin), and fibroblast activity (SMA, proliferation). Implemented as a logic-based ODE system, the network reproduces 82% of the calibration dataset and agrees with independent validation data. Sensitivity analysis reveals a tension-dependent regulation of signaling: at baseline tension, outputs are governed by many boosters (nodes that positively influence downstream targets) and one dominant brake, LATS1/2, whereas at high tension control consolidates and new, tension-specific regulators such as integrin (ITGB1) emerge. Multiple pathway axes converge on a few central regulators, producing pronounced crosstalk, most notably between TGF{beta} and mechanical tension. Finally, linking the collagen outputs to a tissue-level growth formulation yields a bidirectional mechanical-biochemical coupling that reproduces tension-induced skin growth measured in a porcine TE model. This framework establishes a comprehensively calibrated dermal fibroblast signaling network coupled to tissue-level growth, opening opportunities for targeted TE interventions.

Charge neutrality is widely assumed in living cells, yet this approximation breaks down in micron-scale bacteria where charge imbalance and spatial confinement are significant. Using Poisson-Nernst-Planck modeling, we show that unequal cation-anion effectiveness and bounded geometry generate extended intracellular diffuse layers and steady electric fields. We demonstrate that such fields couple directly to intracellular chemical waves, focusing on the Min-protein oscillator of Escherichia coli. Electric-field-driven transport skews the dispersion-mode structure, induces mode crossings, and selectively amplifies Turing and Hopf-Turing instabilities over intermediate length scales, constraining the permitted {omega}-k spectrum and setting optimal wavelengths and modal growth-rate velocities. Experiments in wild-type, anucleate, and antibiotic-treated cells, together with simulations of nucleoid-dependent charge density and field strength, quantitatively validate these predictions and explain observed pattern asymmetries and frequency modulations. Crucially, asymmetric wave-field coupling promotes quasi-periodicity through controlled mode competition, enhancing robustness to noise, cell-size variation, and growth. These findings identify intracellular electric fields as active regulators of biochemical patterning and suggest a general role for wave-field interactions in cellular self-organization.

Snow algae are major biological drivers of snow darkening in polar and high-alpine environments. However, the direct contribution of algal pigmentation to snow reflectance has remained difficult to quantify because field observations cannot disentangle the effects of pigmentation from variation in biomass, species composition, and snow physical properties. Here, we characterized the optical effects of pigmentation using hyperspectral spectroradiometry to compare green, orange, and red cyst-like cells of a snow-derived Haematococcus isolate while controlling for developmental stage and cell abundance. Cysts became more red with increasing astaxanthin concentrations while chlorophyll-a concentrations remained relatively constant. Relative to green cysts, mean reflectance decreased by approximately 30% in orange cysts and 40% in red cysts. Integrated reflectance across the visible spectrum (350-800 nm) was negatively correlated with astaxanthin concentration. These results provide direct experimental evidence that algal pigmentation alone substantially reduces reflectance after controlling for cell abundance and developmental stage, and indicate that differences in snow physical properties may partly obscure this effect under natural field conditions. Our findings identify astaxanthin accumulation as an intrinsic driver of biological snow darkening and suggest that algal pigmentation, which may vary with species identity and physiological state, should be considered alongside biomass when predicting the radiative effects of snow algal blooms.