Interface Focus

Background: Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM2.5) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown. Objectives: To identify urban characteristics associated with EGFR-mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data. Methods: We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m x 300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC). Results: We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR-mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR-mutant status included black carbon (BC), nitrogen dioxide (NO2), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure. Conclusions: Traffic-related pollutants (BC, NO2) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR-mutant lung cancer. These results may inform policy interventions.

Hemodynamic forces play a key role in early cardiac morphogenesis, yet many computational studies assume Newtonian blood behavior. Here, we evaluate the impact of nonNewtonian shearthinning rheology on flow patterns, pressure distributions, and wall shear stress (WSS) during cardiac looping using idealized threedimensional models of the embryonic heart tube. Five geometries representing progressive looping stages, from a linear tube to an Sshaped configuration with ventricular ballooning, were analyzed under pulsatile flow using both Newtonian and powerlaw viscosity models. Across all stages, Reynolds numbers (Re {approx} 1-7) and Womersley numbers (Wo {approx} 0.3) indicated laminar, quasisteady flow consistent with embryonic conditions. Incorporating shearthinning rheology produced substantial deviations from Newtonian predictions, with peak systolic WSS differing by up to [~]40% and pressure drops by up to [~]20%. These effects were most pronounced in regions of increased curvature and geometric complexity. These findings demonstrate that nonNewtonian rheology significantly influences predicted hemodynamic environments during cardiac looping and should be incorporated into computational models aimed at understanding mechanobiological regulation of early heart development.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

Understanding intracranial pressure (ICP) dynamics is essential for interpreting clinical infusion tests used in the diagnosis of cerebrospinal fluid circulation disorders. However, the complex coupling between vascular pulsations, cerebrospinal fluid flow, and intracranial compliance makes quantitative interpretation of these tests challenging. Here, I present a patient specific simulation framework based on an extended electrical analog model that reproduces intracranial pressure dynamics observed during clinical infusion tests. The model integrates physiological inputs including arterial blood pressure, heart rate, respiratory rhythm, and resistance to cerebrospinal fluid outflow derived from clinical data. Built upon the classical Ursino framework, the model incorporates several modifications enabling realistic representation of physiological pulsations and infusion test conditions. The resulting system functions as a hybrid electrical-numerical simulation model representing a simplified digital electrical twin of intracranial hydrodynamics. The model was validated using data from 21 clinical infusion tests performed in patients with suspected normal pressure hydrocephalus. Simulated intracranial pressure recordings were compared with clinical measurements using regression and residual analysis. The simulations demonstrated strong agreement with measured data, with a mean correlation coefficient of r = 0.95 (95% CI 0.94 - 0.96), mean residual values within -1.71 to +1.68 mmHg, and a mean root mean square error (RMSE) of 2.07 mmHg. These results demonstrate that the proposed model accurately reproduces the dynamic behavior of intracranial pressure observed during clinical infusion tests. The framework provides a physiologically grounded computational tool for studying patient specific intracranial dynamics and may support improved interpretation of infusion test results in clinical practice.

Clinical studies of prostate cancer treated with radically hypofractionated highdose-rate brachytherapy (HDR-BT) have reported a significant loss of tumor control that contradicts the standard linear-quadratic (LQ) and low /{beta} ratio paradigm for prostate cancer. In a previous study by our group, we showed that the linear-quadraticlinear (LQL) model could describe this response, but the underlying biological drivers remained unclear. In this follow-up study, we further investigate whether the interplay between hypoxia and reoxygenation kinetics can explain the poor response to extreme hypofractionation. We analyzed a large dataset of 3,239 patients (44 schedules) using a three-compartment reoxygenation model (the MSK model) that simulates the dynamics of oxic, intermediate, and hypoxic cell populations. Results show that the MSK model achieves an excellent fit to the clinical data (p > 0.99) while maintaining a biologically plausible low /{beta} ratio ([≤] 8 Gy). The reoxygenation model provided a performance comparable to the LQL model for low-risk prostate cancer, being slightly inferior to the LQL model to describe the response of intermediate-risk. This suggests that the observed reduction in tumor control is not necessarily a failure of the LQ formalism, but rather a consequence of oxygen dynamics associated with ultra-fractionated schedules, and provides a mechanistic basis for designing clinical trials exploring the response of prostate cancer to ultra-hypofractionation and the role of reoxygenation.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

Quantitative biophysical signatures of nuclear spatial reorganisation across breast carcinoma progression remain insufficiently characterised. We apply two complementary fractal descriptors, Correlation dimension (Dc) and Minkowski dimension (Dm), to 4276 regions of interest across seven breast tissue subtypes from the BRACS dataset, validating observed dimensions against systematically constructed null spatial models to distinguish genuine structural organisation from geometric irregularity. All subtypes significantly exceed the complete spatial randomness baseline, confirming universal departure from random nuclear arrangement. The observed scaling is characterised as statistically monofractal within a bounded pre-fractal range. Invasive carcinoma uniquely fails to exceed the clustered null in Dc while simultaneously showing the weakest Dm null deviation, a dual convergence toward stochastic baselines consistent with the progressive removal of architectural constraints. Flat epithelial atypia exhibits a unique directional dissociation with the lowest Dc across all subtypes combined with high Dm null deviation, a co-occurrence not observed in any other subtype and geometrically consistent with decoupled nuclear spatial organisation at the centroid distribution and boundary morphology scales. Interpreted within a percolation-theoretic framework, the non-monotonic null deviation trajectory maps onto qualitative regime transitions, providing a physically grounded explanation for the observed discrimination profile across pathological transitions. These findings position fractal-like nuclear architecture as a potential descriptor for pre-malignant transitional states.

Arterial thrombosis is initiated when mechanical forces in flowing blood exceed the activation thresholds of platelets and von Willebrand factor (vWF). Despite extensive experimental characterization of shear-induced platelet aggregation, a unified theoretical framework that maps hemodynamic forcing onto clot nucleation is lacking. Here we present Force-Gated Thrombosis (FGT), a non-equilibrium mechanical theory that treats thrombus formation as a continuous phase transition driven by an effective mechanical forcing {Sigma} ={sigma} + |{nabla}{sigma}| + {beta}{varepsilon}, which combines local wall shear stress{sigma} , shear gradient |{nabla}{sigma}|, and extensional strain rate{varepsilon} . We introduce a dimensionless Thrombosis Number {Theta} = ({Sigma}/{Sigma}c)(P/P0)m(C/C0)n, which incorporates platelet concentration P and coagulation factor concentration C, and governs the transition between stable flow ({Theta} < 1) and active clot growth ({Theta} > 1). The thrombus density is represented by a scalar order parameter{varphi} whose dynamics follow a Ginzburg- Landau free energy functional. For a simplified stenosed artery we derive an analytic closed-form thrombosis onset criterion and a critical flow rate [Formula], where{delta} is stenosis severity. Linear stability analysis shows that perturbations grow at rate{omega} (k) = {Lambda}({Theta}) - D{varphi}k2, becoming unstable when {Theta} > 1. Near threshold the clot volume fraction scales as{varphi} [~] ({Theta} - 1)1/2, a mean-field critical exponent consistent with Ginzburg- Landau theory. Systematic comparison with fifteen published experimental and computational datasets spanning shear rates from 100 to 15,000 s-1 confirms that FGT correctly predicts the existence, location, and approximate severity of pathological thrombus formation across diverse vascular geometries. The theory provides a quantitative bridge between single-molecule mechanobiology and macroscale clinical thrombosis, and yields experimentally testable predictions distinguishing FGT from purely biochemical models.

Biofilms formed by soil microbes hold immense potential for bioremediation, carbon dioxide sequestration, and the development of sustainable cementitious materials. However, quantifying the complex temporal coupling among bacterial growth, extracellular matrix (ECM) production, and mineralization dynamics remains a significant challenge due to the inherent nonlinearity of these processes and signal noise in high-throughput assays. To address this, we utilized an automated real-time kinetic analysis framework integrating connectivity-based segmentation, automated baseline alignment, and robust sliding-window algorithms to quantify the biomineralization competence of Bacillus subtilis under varying calcium regimes. Crucially, our results demonstrate that calcium carbonate promotes microbial growth as effectively as the highly soluble calcium acetate, providing strong evidence that B. subtilis actively solubilizes this crystalline powder to facilitate its metabolic requirements. Despite this growth efficacy, we found that calcium carbonate is an inadequate source for macro-calcite production compared to organic salts. By quantifying the expression efficiency of the sinI reporter gene, we determined that calcium-acetate-driven ECM expression significantly enhances the structural compatibility required for robust biomineralization. Furthermore, kinetic modeling suggests that ECM overproduction can partially compensate for defects in crystal growth-when provided crystalline calcium carbonate powder. These findings, enabled by high-resolution automated signal processing, underscore the critical role of self-mediated carbonate supply and present new engineering pathways for upcycling mineral-rich construction waste.

Wastewater-based surveillance (WBS) is increasingly used to monitor infectious disease dynamics, yet most evaluations focus on correlation or forecasting - neither of which directly assesses whether wastewater signals can identify the epidemiological events most relevant to public health decision-making. We argue that outbreak onset and epidemic peak detection are the operationally critical use cases of WBS, requiring a fundamentally different evaluation framework. We introduce a classification-based framework that treats WBS as an event-detection problem, defining outbreaks and peaks as discrete events, establishing detection intervals to account for timing uncertainty, and incorporating censoring and data completeness criteria for valid comparisons against imperfect clinical reference outcomes. Within this framework, we apply a Bayesian exponential growth model for outbreak detection - benchmarked against a standard reproductive number (Rt)-based method - and a rule-based algorithm for peak detection, evaluating performance via sensitivity and positive predictive value (PPV). Applied to county-level SARS-CoV-2 wastewater data from 281 U.S. counties (Biobot, 2021-2024), the exponential growth approach substantially outperforms the Rt-based baseline: sensitivity 0.82 and PPV 0.64 versus sensitivity 0.58 and PPV 0.19 for the best-performing Rt variant. Peak detection achieves sensitivity 0.84 and PPV 0.70 at the county level. Both peak and outbreak detection achieve strong and consistent performance against hospitalizations and deaths at the state level. Spatial aggregation yields a statistically significant improvement in peak detection PPV against a curated reference standard ($p < 0.001$), while outbreak detection improvements under aggregation are directionally consistent but not statistically significant. Wastewater leads case-defined outbreaks by 4-6 days but minimally leads epidemic peaks, consistent with wastewater approximating prevalence rather than incidence. These findings demonstrate that wastewater signals can reliably detect outbreak onset and epidemic peaks across spatial scales and clinical outcomes, and that the choice of detection method matters substantially in practice. The classification framework developed here provides a reusable and principled tool for evaluating any surveillance signal as an event-detection system, with direct relevance to how WBS is actually used in public health decision-making.

Wastewater-based epidemiology has emerged as a powerful complement to clinical surveillance for monitoring infectious disease dynamics. However, most existing approaches either treat wastewater sites in isolation, overlooking spatial dependencies, and often fail to account for variability in data quality, limiting their ability to generate reliable predictions of healthcare demand. Here we present a spatial Bayesian renewal framework that integrates wastewater surveillance with mobility-informed spatial interactions while incorporating reliability-weighted wastewater signals. We apply the framework to three major respiratory pathogens, i.e., SARS-CoV-2, influenza, and respiratory syncytial virus (RSV), using wastewater and hospital data from counties in South Carolina. Across rolling four-week forecasts, the spatial framework consistently outperforms non-spatial approaches and remains robust even in counties lacking direct wastewater or hospitalization observations. Importantly, we show that county-level forecasts can be translated into facility-level predictions, enabling localized assessment of healthcare demand. These forecasts provide actionable early-warning signals to support hospital capacity planning, staffing decisions, and resource allocation. Together, this work establishes a scalable digital surveillance framework that integrates heterogeneous data sources for enabling more reliable infectious disease forecasting and supporting public health decision-making in underserved and data-limited settings.

The endotracheal tube resistance dominates the total airway resistance in most intubated patients. Mucus deposition and biofilm formation can rapidly increase tube resistance and thereby contribute to serious ventilatory impairments, including dynamic hyperinflation, intrinsic PEEP build-up, added work of breathing, and patient-ventilator asynchrony. During controlled mechanical ventilation, an increased tube resistance can be inferred from the difference between peak and plateau pressure, but this approach fails during pressure-supported spontaneous breathing. Here, we present a method that estimates the linear and nonlinear components of tube resistance from naturally occurring airway pressure and flow fluctuations at the airway opening, without a tracheal pressure sensor and without applying mandatory forced oscillations. This is achieved by solving the equation of motion using band-pass filtered airway pressure and flow signals. Band-pass filtering isolates the relevant resistive and inertive pressure losses across the tube by removing slow contributions from muscle pressure and lung elastance as well as high-frequency noise. The method accurately recovers both linear and nonlinear tube resistance parameters with < 10% error and < 2% bias. Moreover, it enables real-time implementation of full Automatic Tube Compensation (ATC), even in the presence of severe tube obstructions. Continuous estimation of endotracheal tube resistance from naturally occurring airway pressure and flow fluctuations enables real-time detection of clinically relevant tube narrowing and may help improve patient safety, reduce patient-ventilator asynchrony, and facilitate weaning.

Developing parsimonious, mechanism-aware quantitative models that predict how biological effectiveness changes with different modifiers remains, in general, an unsolved problem. Advances in radiobiological research have created a large knowledge base of first-principles mechanistic models of radiation response that, in principle, could accurately predict radiosensitivity across different experimental and clinical conditions. However, in practice these mechanistic models come with an overabundance of parameters, the majority of which are practically unidentifiable and, moreover, likely unnecessary if one simply wishes to predict how radiosensitivity changes for some specific modifier of interest. Nevertheless, determining which few details in the full mechanistic model are relevant for a given purpose, as well as how to remove any other extraneous details, remains a highly non-trivial task. In this study, we demonstrate the potential of model reduction, starting from a detailed mechanistic description, as a systematic strategy for deriving parsimonious, experimentally falsifiable radiobiological descriptors. As a proof-of-concept demonstration, we apply the Manifold Boundary Approximation Method (MBAM) to a Mechanistic Model of DNA Repair and Survival (MEDRAS), for the problem of cell survival prediction following an acute exposure. Our findings reveal that the complete MEDRAS model for an arbitrary mixed-quality exposure can be structurally simplified to a reduced three-parameter model for an effective uniform-quality, named MEDRAS-LPL. Additional MBAM analysis on MEDRAS-LPL identifies two boundaries in parameter space, corresponding to sparsely ionizing and densely ionizing radiation. Mapping of MEDRAS-LPL parameter space on to effective LQ space further demonstrates that parameters close to the sparsely ionizing boundary line up with expectations from the theory of dual radiation, while parameters close to the densely ionizing boundary line up with expectations from a purely linear model based on a target-theory description. Moreover, our formalism predicts enhanced synergistic interactions between sparsely ionizing and densely ionizing radiation beyond the Zaider Rossi model (ZRM) paradigm, in line with empirical observations. The results highlight the potential for using reduced-order models not only for predictive applications but also for generating novel hypotheses that can inform future experimental designs and optimization strategies in radiobiology.

During vertebrate development, the flat surface of the gut epithelium undergoes a dramatic transformation into densely packed arrays of finger-like projections called intestinal villi. Recent studies show that the villus formation relies on a tissue dewetting process, in which mesenchymal tissues buckle the overlying epithelial layer into periodic folds. However, the mechanisms driving subsequent elongation of these folds into finger-like villi remain largely unexplored. Here, we propose a simple mechanism for villus elongation that couples tissue dewetting to cell differentiation, which emerged as a repeated outcome of multiple independent simulations of an evolutionary-developmental Cellular Potts Model. In this mechanism, a liquid-like mesenchymal tissue continuously differentiates into a solid-like mesenchymal tissue at the interface between them. This differentiation drives the liquid-like tissue to continuously retract from the solid-like tissue in the opposite direction of the interface through dewetting, ultimately creating a finger-like projection. A merit of our proposed mechanism is that it only requires two tissues with different viscosities, high surface tension, and cell differentiation. We develop a simplified phase-field model to determine exactly how villus morphology depends on these three requirements. Since these requirements are satisfied not only in intestinal villi but also in many other developing tissues, we propose that the same mechanism could also drive the elongation of other tissues.

BackgroundSubcutaneous oncology monoclonal antibodies (SCOmAbs) offer significant benefits compared to intravenous formulations, including reduced administration time and potential for self-administration. However, little has been published on real-world preparation and administration practices with these novel formulations. MethodsThis was a qualitative, exploratory study of 30 participants (10 patients receiving SCOmAbs, 10 nurses, and 10 pharmacists/pharmacy technicians) across various U.S. healthcare facilities. One-on-one, in-depth interviews examined current practices, pain points, device integration potential, projected impact of increased SCOmAb adoption, and perspectives on home administration. ResultsFindings revealed considerable variability in SCOmAb preparation and administration practices. While preparation methods generally aligned with typical parenteral workflows, notable deviations included bedside preparation by nurses (7/30), use of syringe pump modules (3/19), and one case of patient self-administration at home. Most participants utilized closed-system transfer devices (12/22) despite inconsistent hazardous drug treatment between facilities. Administration challenges included ergonomic difficulties for nurses during manual push (5/9 reporting physical discomfort) and variable injection techniques to accommodate patient comfort. Nurses reported significant workflow impact from being "tethered" to patients during administration, which could require staffing adjustments as SCOmAb frequency increases. Most patients (6/9) expressed interest in home administration for potential time savings and flexibility, though concerns about training, support, and safety were common. ConclusionsAs SCOmAb utilization expands, facilities may face barriers associated with increased demand that could necessitate innovative solutions. To leverage the full potential of SCOmAb products, developers should consider how next generation product presentations might minimize identified pain points, streamline administration, and facilitate safe transition to home self-administration. Delivery device integration, whether through prefilled syringes, portable infusion pumps, or other delivery systems, may address current challenges but requires careful consideration of facility infrastructure, product complexity and usability, workflow impact, and patient training requirements.

Uncertainty quantification (UQ) in computational heart models is essential for reliable cardiac digital twins (DTs) in personalized medicine, yet remains challenging. Traditional Monte Carlo and stochastic Galerkin methods often become impractical in the high-dimensional, nonlinear state variable and parameter spaces of cardiac electrophysiology and mechanics. This article introduces a framework for learning a joint probability density over cardiac observables and model parameters, enabling the characterization of statistical dependencies across a large number of variables in patient-specific cardiac DTs. By sampling from this density and conditioning on available data, useful predictive distributions can be constructed, allowing uncertainty to be propagated through the model and quantified in terms of variability. Conditional regression can then be performed directly on this learned density, enabling systematic exploration of interdependencies among observables for both predictive inference and model design. The statistical methodology adopts a geometry-aware generative learning framework, recently introduced by the authors, that decouples the learning of data geometry from sampling. First it identifies a low-dimensional latent representation that captures the intrinsic structure of the data and its multiscale geometric features. A stochastic differential equation is then formulated directly in the low-dimensional latent space to generate samples efficiently; these are subsequently mapped back to the high-dimensional space of cardiac states and parameters through a smooth lifting operator. We demonstrate the approach on a ventricular arrhythmia prediction benchmark, where the learned joint probability density enables the construction of predictive distributions over key parameters (e.g., conductivities, fibrosis patterns) through sampling and conditioning. This enables uncertainty to be propagated and quantified through sampling and conditioning on the learned joint density, with substantially fewer model evaluations than conventional UQ methods.

BackgroundPrevious machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such as DeepGlioma, have achieved high performance (91.5% accuracy) on curated datasets. However, when used intraoperatively, DeepGlioma (162M parameters) runs slowly on current SRH hardware and underperforms due to its lack of an image rejection mechanism and its validation on curated images. Here, we introduce SRH-Informed Glioma classificatioN with Attention Learning (SIGNAL) (27M parameters), a lighter model with a built-in attention-based rejection mechanism that outperforms DeepGlioma on uncurated clinical datasets. MethodsSIGNAL was developed using 1.56 million SRH fields-of-view from 967 adult diffuse glioma patients collected between December 2017 and July 2025. We used 412 patients from NYU for training and internal validation and a multi-institutional, international cohort of 555 patients for testing. SIGNAL uses a ResNet50 backbone pretrained using a hierarchical contrastive loss function followed by a multi-head multi-layer perceptron (MLP). Using a patch-based attention threshold of 0.6, a final MLP was trained to predict glioma subtypes: glioblastoma, oligodendroglioma, or astrocytoma. ResultsSIGNAL outperformed DeepGlioma, achieving greater overall accuracy (90.10% vs. 72.59%) while running faster (16.0 vs. 6.7 patches/s). SIGNAL also outperformed DeepGlioma on all three molecular classification tasks, including IDH mutation (accuracy: 93.51% vs. 79.22%), 1p19q codeletion (93.51% vs. 88.31%), and ATRX loss (89.61% vs. 83.98%). SIGNALs attention mechanism had a strong positive linear correlation with mean patch cellularity (r=0.96, p<0.001) and a strong negative correlation with patch blood coverage (r=-0.99,p<0.001). Finally, subtype and molecular accuracy between tumor core and margin samples were equivalent despite significantly lower patch retention in tumor margins (44.5% vs 60.2%, p<0.0001). ConclusionSIGNAL is a lightweight model for intraoperative molecular classification of gliomas using SRH imaging. Its attention-based image quality filter allows for excellent performance, quick processing, and highly interpretable outputs critical for reliable use in intraoperative workflows. Brief 1-2 Sentence DescriptionWe present SIGNAL, a lightweight machine learning model for intraoperative molecular classification of diffuse gliomas using stimulated Raman histology, whose core innovation is a learned attention mechanism that filters diagnostically uninformative tissue, such as blood and acellular regions, before classification, enabling robust real-world generalizability. Validated on 555 patients across four international centers, SIGNAL outperforms the previous state-of-the-art model DeepGlioma on glioma subtype classification (90.10% vs. 72.59% accuracy) while running 2.4 times faster on intraoperative hardware.

Airborne transmission of respiratory pathogens depends on their ability to remain viable in drying respiratory droplets, yet the physicochemical drivers of bacterial inactivation during droplet evaporation remain poorly quantified. This study combines controlled droplet experiments with physicochemical modeling to investigate how osmotic pressure dynamics influence bacterial survival. Using Escherichia coli and Staphylococcus epidermidis as Gram-negative and Gram-positive surrogates, respectively, we measured viability loss in artificial saliva droplets dried at multiple relative humidities and reconstructed the time-resolved osmotic pressure using the Respiratory Aerosol Model (ResAM). Both organisms remained stable while droplets were liquid but lost viability following efflorescence, when rapid solute concentration changes produced sharp osmotic pressure increases. The extent of inactivation scales log-linearly with the rate of osmotic pressure change around efflorescence: E. coli decays faster than S. epidermidis, and relationships derived in artificial saliva predict survival in independent phosphate-buffered saline experiments. A more rapid drop in humidity led to more severe osmotic shocks and greater inactivation. These results identify the rate of osmotic pressure change during efflorescence as a quantitative, medium-independent predictor of bacterial survival in drying respiratory droplets. ImportanceAirborne infection risk depends on how long microorganisms remain viable in respiratory particles after exhalation, yet the physical mechanisms controlling bacterial survival during droplet drying are not well defined. Evaporation of respiratory droplets concentrates salts and can impose sudden and extreme osmotic stress on microbes, but this process has been difficult to quantify because osmotic pressure cannot be measured directly inside microscopic droplets. Integration of droplet experiments with a physicochemical aerosol model shows that bacterial inactivation is governed primarily by the rate of osmotic pressure increase during droplet efflorescence rather than by static values of humidity or solute concentration alone. This mechanism explains why rapid drying may produce strong inactivation.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

Time-resolved measurements are central to calibrating mechanistic dynamical models, but current inference frameworks typically assume that reported measurement times are exact. In practice, actual sampling times may deviate from reported times because of sample-handling delays, imper-fect synchronization, or reporting errors. Here, we present a Bayesian framework for parameter inference in ordinary differential equation models that explicitly accounts for uncertainty in measurement times. We formulate latent measurement times as random variables and derive a joint and marginalized posterior. To compute the marginal likelihood efficiently, we augment the original dynamical system with additional state variables that evaluate the required integrals during numerical simulation. This reduces the dimensionality of the estimation problems and allows for efficient and reliable Markov chain Monte Carlo sampling. Across synthetic examples and a published model of carotenoid cleavage in Arabidopsis thaliana, neglecting time uncertainty led to biased estimates and overconfident uncertainty quantification, whereas the proposed marginalized formulation recovered reliable parameter estimates while substantially improving sampling efficiency and scalability. These results identify measurement time uncertainty as an important source of variability in dynamic modeling and establish posterior marginalization as a practical strategy for robust mechanistic inference.