PNAS Nexus

Incidence of type 2 diabetes is increasing at ages when education, work, family, and financial transitions are taking place, yet we lack robust evidence of whether earlier treatment changes life-course outcomes and over which time span this takes place. This paper uses the medical cutoff for diabetes diagnosis (HbA1c of 6.5 percent) as a natural experiment to study the effects of diabetes treatment using electronic health records (EHR) and panel data. This paper has three main findings. First, using EHR data, we find that there is a sharp increase in the probability of both diagnosis of diabetes and prescription when the HbA1c equals 6.5 percent. Second, we find that treating diabetes reduces HbA1c levels, weight, BMI, and blood pressure and increases the amount of care received, proxied by the number of HbA1c tests. Both the diagnosis and a prescription are independently able to produce positive changes in metabolic health, although a prescription is more effective in this regard. Third, we conclude that treating diabetes does not have a significant effect on life-course outcomes for a cohort of young Americans aged 24-32, although it does result in a reduction in HbA1c levels that are seen even eight years after the intervention. Taken together, these findings suggest that receiving a diagnosis and prescription are both effective treatments for diabetes, but they do not translate to significant alterations in the lives of young adults in the medium-term.

Nickel has been studied for a long time as an environmental contaminant but less so in its connection to population health. It does not announce itself as loudly as its transition metal brethren like mercury and cadmium, but its chemical properties permit it to be deleterious as a low-dose, chronic exposure, particularly among those with immune systems sensitized to it. There is a growing evidence base and vocabulary to discuss nickel's affect on health. However, in the U.S., there are not recent, reliable estimates of the share of the population with a nickel allergy, let alone how much nickel Americans are exposed to through their diet. This paper seeks to close this evidence gap by creating a new dataset of dietary nickel and other heavy metal exposure and assessing how high levels of dietary nickel exposure shape local demand for health care services. We use soil data from the U.S. Geological Survey and data on agricultural product transport from FoodFlows.org to create a county-level dietary nickel exposure index. We then use a large electronic health record database and double machine learning to estimate how demand for primary care services varies across levels of dietary nickel exposure. We find that counties with high nickel exposure experience an increase in the share of primary care office visits for symptoms highly suggestive of nickel poisoning. This result survives multiple hypothesis test corrections and placebo tests. Our research suggests that nickel has harmful effects on individual health whose exposure can be measured at a population level, and is shaping primary care across the U.S.

Socioeconomic, demographic, and health system structures may have shaped COVID-19 pandemic impact across populations, but past analyses typically examined few factors. We systematically examined correlates of COVID-era excess mortality, considering 2,745 county-level variables of demography, race/ethnicity, income, insurance, education, employment, housing, and health system. Pearson correlation coefficients (CCs) were obtained for the most recent available pre-pandemic value against age-standardized county excess-death for each year during 2020-2024. Counties were population-weighted. Variables were grouped by meaning into 11 semantic super-clusters. Overall, 17.3% of variables reached at least a moderate correlation level (|CC| > 0.30) and 2.8% reached strong correlations (|CC| > 0.45). Strongest correlations were seen for college attainment (CC -0.54), uninsurance among adults 40-64 (+0.53), and high income (-0.53). At least moderate correlations were seen for 9.1% of variables in 2020 and 8.5% in 2021, but only 1.8%, 0%, and 1.3% in 2022, 2023, and 2024, respectively. Similar patterns of concentration of moderate correlations in the first two pandemic years appeared in both elderly and non-elderly populations. Of 472 variables with |CC| > 0.30, 362/395 moderate-band and 77/77 strong-band variables belonged to demography and socioeconomic super-clusters. Only 7% of health system variables reached |CC| > 0.30, versus 31% of socioeconomic and demographic variables. Using the most recent available value until 2023 or 2015, different population weighting, and Spearman correlations yielded similar results. Overall, these ecological analyses suggest strong relationships of socioeconomic structure and demographics rather than health-care resources/supply with excess mortality across US counties especially during 2020-2021.

Understanding muscle tissue is key not only for advancing our knowledge in biophysical function of the human body, but can also open new paths to better description of pathological states, improve treatment and training and even provide insights for drug development. While using excised muscles is an excellent way to study muscular function, this limits access to non-human tissue, as human muscle tissue donation is very limited. The past decade has seen a rise in 3D in vitro models of human skeletal muscle, however many current experimental approaches to create such models often struggle with the high variability of primary cells or the limited translational relevance in cases where murine lines are used. Furthermore, the state-of-the-art functional analysis is typically focusing at peak force, which overlooks critical kinetic information. In this study, we present an systematic approach to generate functional engineered skeletal muscle tissues (ESMs), measure the contraction force dynamics and model these with a new approach to extract the effect of a series of standard pharmacological modulators. Within 14 days, LHCN-M2 ESMs mature into aligned, multinucleated myofibers expressing key sarcomeric markers and exhibiting robust excitation-contraction coupling. To decode these dynamics, we introduce a novel mathematical model based on stretched exponential functions. This framework accurately captures the heterogeneous contraction and relaxation phases across diverse phenotypes using only six interpretable parameters. We validated the platforms sensitivity using a library of pharmacological modulators. Our kinetic modeling revealed a distinct parameter fingerprint for different drug classes such as the specific changes of contraction kinetics that peak force alone could not detect. Additionally the presence of the necessary drug targets as well as the state of maturation were investigated by proteomic profiling. Together, study provides a scalable, high-fidelity human platform for high-content pharmacological screening and muscle biophysics.

A successful pregnancy hinges on a finely coordinated dialogue between the maternal endometrium and the implanting embryo. Following embryo attachment to the uterine epithelium, underlying stromal cells undergo a transformation into decidual cells that promote a vascularized maternal-fetal interface and direct trophoblast lineage decisions through paracrine cues. However, the metabolic adaptations that enable decidual cells to support these energetically demanding processes remain poorly understood. Here, using a uterine-specific knockout mouse model, we identify Glucose Transporter 1 (Glut1) as a critical metabolic regulator linking endometrial glucose uptake to reproductive success. We demonstrate that stromal Glut1, induced by hypoxia-inducible factor 2 (Hif2), sustains a Hif2-Rab27b feed-forward circuit that drives vesicular trafficking during pregnancy through the glucose-sensing transcription factor MAX-like protein X (Mlx). Mice lacking endometrial Glut1 are severely subfertile despite normal embryo attachment. Glut1-deficient uteri exhibit impaired stromal extracellular vesicle secretion, defective decidual angiogenesis, and marked dysregulation of trophoblast differentiation, including expansion of trophoblast progenitors, accumulation of glycogen trophoblast cells, and altered placental lactogen production. These placental defects culminate in mid-gestation fetal loss and maternal gestational diabetes mellitus (GDM). Collectively, our findings establish endometrial Glut1 as a metabolic gatekeeper of maternal glucose homeostasis and placentation and introduce a genetically tractable mouse model of spontaneously developing GDM, a disorder affecting nearly one in seven pregnancies worldwide. SIGNIFICANCEThis study uncovers endometrial Glut1 as a previously unrecognized metabolic regulator of placentation, demonstrating that maternal stromal glucose uptake dictates trophoblast fate and maternal glycemic control, and providing the field with its first genetically defined mouse model of spontaneously arising gestational diabetes.

Rainfall and vapor pressure deficit (VPD) are well-studied hydrological variables that largely determine aboveground net primary production (ANPP) in most ecosystems. Meanwhile, the impacts of another important part of the hydrologic cycle, non-rainfall water from fog and dew, remain poorly understood at the ecosystem level. To fill this gap, we used meteorological variables measured at weather stations along with satellite-derived vegetation greenness data from surrounding areas to examine how fog and dew frequency affect summer plant growth across the contiguous United States. Our analysis shows that, even after accounting for precipitation, VPD, and land-cover type, fog and, more so, dew enhanced vegetation productivity in water-limited regions. In contrast, non-rainfall water had a neutral or negative impact on plant growth in humid regions, with fog showing the strongest and most widespread negative effects. Taken together, our findings reveal that summertime non-rainfall water has differential effects on vegetation that are largely determined by ecosystem-level water availability. These aridity-dependent effects of fog and dew should be considered in future ecological and agricultural studies and in assessments of projected climate impacts on vegetation.

Capabilities for quantitative monitoring of chronic wounds remain an unmet clinical need, as existing diagnostic approaches rely on semiquantitative evaluation of symptoms that lack sensitivity especially during early stages of infection. Here we present a scheme for tracking wound physiology that leverages a miniature, wireless skin-interfaced device for non-contact, transient measurements of the flux of volatile organic compounds (VOCs) and water vapor from the wound microenvironment. Unlike emerging smart bandage platforms that rely on physical contact with the fragile wound bed to interrogate liquid-phase biomarkers, this strategy uses an engineered microclimate and suspended suite of sensors to measure the diffusive transport of wound-derived gases across the wound surface but separated from it. The result enables quantitative evaluation of metabolic activity and healing progression without perturbing the healing tissues. In biofilm growth models of Staphylococcus aureus, measurements demonstrate that trends in VOC flux correlate strongly with bacterial growth kinetics and precede any visible biofilm formation. Longitudinal monitoring in infected murine wound healing models shows that concurrent measurements of water vapor and VOC flux provide complementary physiological insights, capturing both the trajectory of barrier restoration and the dynamics of bacterial burden. The findings establish this non-contact sensing scheme as a distinct and clinically translatable paradigm for wound monitoring, with broad implications for non-invasive surveillance of disease states in which tissue metabolic activity and skin barrier integrity serve as actionable physiological readouts. Significance StatementLimited capabilities in continuous, quantitative assessment of a wound make early diagnosis and effective management challenging, particularly in cases of infection that rapidly progress before symptoms appear. Non-contact approaches for wound monitoring that preserve fragile tissue can transform wound care. In this context, gaseous flux from the wound bed provides an integrative measure of microbial activity and barrier restoration. This study establishes a wearable sensing platform that quantifies these fluxes in real time, enabling early infection detection and temporal tracking of wound healing. These results highlight a path toward personalized treatment strategies and reduced reliance on episodic clinical evaluation.

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.

Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation analyses were subsequently performed in latent space to evaluate indirect associations between socioeconomic disadvantage, psychosocial factors, and multimorbidity. The final analytic cohort included 20,804 participants with complete multimodal data. Across 800 exposure--mediator--outcome combinations, mediation signals were concentrated within a small number of latent dimensions. The strongest indirect association linked a socioeconomic disadvantage dimension, a psychosocial vulnerability dimension, and a cardiometabolic multimorbidity dimension (NIE = 0.002517). The psychosocial dimension was characterized by poorer mental health, greater loneliness, lower social well-being, and lower health literacy, whereas the outcome dimension was associated with hypertension, diabetes, hyperlipidemia, obesity, chronic kidney disease, and heart disease. Bootstrap analyses supported the stability of the leading pathway. These findings suggest that psychosocial vulnerability may contribute to the association between socioeconomic disadvantage and cardiometabolic multimorbidity. More broadly, the proposed framework illustrates how AI-based representation learning can be used to investigate complex relationships across high-dimensional multimodal health data.

Biological inheritance can be treated as a class of catalytic templating reactions in which a daughter molecule, generated by a kinetic kernel acting on a parent template, is itself a substrate for the next round of the same catalysis. We give the physicochemical conditions under which such a reaction can support unbounded heritable molecular distinguishability. Four conditions on the template-operator pair organize the analysis: nonzero per-site information content under the activesite recognition kernel (R1), a count of independently variable recognized positions that grows without bound as the reaction extends (R2), catalytic closure under iteration, possibly through a reversible involution such as Watson-Crick complementation (R3), and stochastic drift of the kernel in its recognition alphabet (R4). A fifth, scope-defining condition (R5) restricts the principle to kernels that are intrinsic physicochemistry rather than externally optimized search. These conditions are necessary for two distinct outcomes, separable as two necessity results. The capacity theorem states that linear scaling of substrate Shannon capacity with reaction extent requires R1, R2, and R3 but not R4: a perfect copier transmits an exponentially large configurational ensemble while producing no novelty. The generation theorem states that diversification of the heritable configuration set beyond the deterministic closure of a finite initial repertoire additionally requires R4, because branching trajectories in the recognized alphabet are what produce innovation. Populationlevel kinetics follow as a corollary that organizes six attested templating reactions into a taxonomy, and as a finite-population, finite-horizon proposition tested over five inheritance kinetic schemes, in which only individual-level stochastic drift reaches the target within the model class. We test the framework on the recently characterized bacterial defense system Drt3b, which makes alternating poly(AC) DNA without using a nucleic acid template. The framework classifies Drt3b as a cyclic two-state catalytic templating channel with a 1-bit capacity ceiling, and predicts that the Glu26-to-Gln active-site mutant incorporates dG at 10% probability at the dA-selecting state; the published biochemistry reports 10.16%. Across 1,232 Drt3b homologs, the framework predicts and recovers a 15.7-fold elevation of dG misincorporation in six clades carrying the natural Glu26-to-Asp substitution at this gate. Substitutions at two universal gate residues, Arg253 (architectural) and Gly248 (selectivity), provide single-experiment site-directed mutagenesis tests of the frameworks predictions. POPULAR SUMMARYA bacterial defense protein called Drt3b, recently characterized in E. coli, synthesizes DNA with a strict alternating ACAC pattern without copying any template. Two conserved active-site residues, Glu26 and Arg253, are modeled as enforcing an alternating two-state catalytic cycle that selects which nucleotide enters at each step. This is sequence without a sequence template, and it does not fit the textbook picture of inheritance. We treat inheritance as a class of catalytic templating reactions and ask which chemistries can support openended evolution. Two distinct requirements emerge. Capacity, the ability to transmit exponentially many distinct heritable configurations, requires three conditions on the template-catalyst pair: more than one monomer state recognized at each position, a position count that grows unboundedly with the reaction extent, and applicability of the catalysis to its own product. Generation of novel heritable configurations beyond what is already present requires a fourth condition: stochastic drift of the catalysis in its recognition alphabet. A perfect copier has capacity but cannot innovate; a drifting copier has both. Drt3b fails the second capacity condition, because its cycle has two states regardless of product length. The framework classifies six attested biological templating reactions as instances or partial instances of the same chemical specification, and it identifies two single-residue substitutions at Drt3bs active site whose measured effects would test its predictions.

Air pollution has well-documented negative cardiovascular and respiratory consequences. However, the impact of particulate matter pollution (PM2.5) on brain development is unclear. Animal studies suggest that exposure to early-life PM2.5 can cause adverse neurodevelopmental outcomes, but in vivo human work has been hampered by cross-sectional designs and heavily confounded PM2.5 exposure measures. Here we use an innovative natural experimental design to isolate the effects of wildfire pollution on neurocognitive development in a large cohort of children (N>9000, 4 waves, age 9-16). Doing so, we find that greater wildfire PM2.5 exposure is robustly associated with slower brain development and shallower cognitive improvement across early adolescence. Our study underscores the urgent public health concern that wildfire PM2.5 poses for childhood development.

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.

The complex interplay between extracellular matrix stiffness and actomyosin contractility regulates long-term integrin-based adhesion and signaling in mammalian cells. However, how cells sense and respond to the stiffness of the environment during the first minutes of initiating adhesion remains elusive. Here, we show that fibroblasts upon initiating adhesion to fibronectin switch between two distinct mechanosensitive adhesion modes. The first mode of "slow" adhesion strengthening, shared between 5{beta}1 and v{beta}3 integrins and depends modestly on fibronectin stiffness. Fibroblasts adapt this slow adhesion strengthening mode, which is independent of actomyosin contractility and intracellular signaling, on soft fibronectin substrates (<5 kPa). On stiff fibronectin substrates (>5 kPa), however, 5{beta}1 integrins but not v{beta}3 integrins switch to a "fast" adhesion strengthening mode that considerably strengthens adhesion within seconds. The switch to the fast mode depends on myosin II-mediated contractility and a mechanosensitive signaling hub that includes the 5{beta}1 integrin-FN catch bond, paxillin, and focal adhesion kinase. The mechanistic findings highlight the similarities and differences of integrin-type specific adhesion strengthening and intracellular regulation, which depend on mechanotransduction in fibroblasts during adhesion initiation.

What drove nucleic acids (NA) to associate with proteins (PR) at the Origins of Life? We reason from polymer physics and the Central Dogma (CD) that the fitness value of cooperating through a division of labor - NA for replication fidelity and PR for functional fitness - is much higher than for either polymer alone. Our model shows a Pareto Front, where NA and PR can bootstrap each other to achieve autocatalytic cooperativity towards biology.

The introduction of salt iodization is associated with improved health and socioeconomic outcomes, but is not yet universally adopted and not always sustained. Using a quasi-experimental event study with difference-in-differences over space and time, we quantify the impacts of iodine deficiency in utero and infancy on childhood mortality and later academic achievement in Ethiopia, comparing cohorts born just before and after the May 1998 border closure that interrupted access to iodized salt. Rural children with fewer months of early-life exposure to iodized salt scored lower on standardized secondary-school exams, especially in districts with low environmental iodine, with excess deaths emerging in infancy and persisting through early childhood. These findings reveal the long-term benefits of salt iodization for health and education, especially for people with low intake of iodine from environmental sources.

SignificanceDigital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool for studying cellular morphology and dynamics. While most DHM studies have focused on cell-level analysis, the quantitative characterization of neuronal network organization and maturation from DHM images remains largely unexplored, highlighting the need for dedicated computational approaches. AimWe aimed to develop an automated framework combining deep-learning-based image analysis and graph theory to quantitatively characterize the organization, connectivity, and maturation of neuronal networks in primary rat cortical cultures imaged by DHM. ApproachTwo U-Net convolutional neural networks were trained on manually annotated DHM phase images to segment neuronal cell bodies and neurites. The resulting segmentation maps were used to infer putative morphological connections between neurons and generate graph representations of neuronal networks, referred to as graph fingerprints. A panel of 18 connectomics-inspired graph features was then computed to characterize local and global properties of network organization across four stages of culture maturation. ResultsThe mean area under the receiver operating characteristic curves was 0.98 for cell-body and 0.91 for neurite segmentation, indicating near-perfect identification. Graph-theoretical analysis revealed reproducible topological changes during network maturation in vitro, including increased density, reduced modularity, and progressive network integration. Correlation analysis showed that the 18 graph features grouped into two highly correlated families. A Random Forest classifier identified density and modularity as the most informative descriptors, achieving an accuracy of 87% in classifying maturation stages of neuronal cultures. ConclusionsOur results demonstrate that combining DHM, deep-learning-based segmentation, and graphtheoretical analysis enables quantitative characterization of neuronal network organization and maturation from label-free phase images. This framework provides a foundation for future studies of pharmacological experiments, neuronal network phenotyping, and human induced pluripotent stem cell (hiPSC)-derived neuronal cultures, where quantitative assessment of network organization remains a major challenge.

Like mammals, some reptiles possess sensitive and frequency-selective hearing at high frequencies. Both groups employ a place-frequency map within the inner ear to encode sound stimuli, but the mechanisms by which they achieve tuned nerve activation along the cochlea are believed to be distinct. To investigate the mechanical origins of auditory sensation in the reptiles, we measured sound-evoked displacement responses in the hearing organ of the tokay gecko (Gekko gecko), known as the basilar papilla. Using optical coherence tomography, we were able to resolve sub-nanometer-scale vibrations throughout the organ both in vivo and ex vivo. We found that tuning was not present in the tissue-scale mechanics at any position within the organ; it instead exhibits an untuned rotational motion. We developed a mathematical model which predicted that this motion induces hair-bundle-stimulating fluid velocity, and is due to an anatomical asymmetry seen in many reptile and bird species. These results suggest that hair-bundle-level mechanics are primarily responsible for tuning in the tokay gecko cochlea, and we argue that our proposed mechanisms generalize to many other species across the Reptilia class. SIGNIFICANCEWe present the most comprehensive picture of tissue-scale mechanics in a reptile hearing organ to-date: sound-evoked displacement responses within the basilar papilla of the tokay gecko (Gekko gecko) both ex vivo and in vivo, across the animals auditory frequency range, and along all three spatial dimensions. We find that place-frequency tuning is not present in the mechanics of the tissue as it is in mammals. Instead, this organ relies on an untuned rocking mechanism to stimulate hair bundles. A mathematical model shows that this may be at play in other reptile and bird species, indicating a unifying mechanism of hearing in the class Reptilia in which hair bundle mechanics play the leading role in frequency tuning.

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.