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Recent advancements in tomography produce imaging data of geological materials (rocks and fossils) at trillion-voxel scales with multi-channels. Such high-resolution datasets are potentially keys to unveil evolutionary biological information with various shapes and sizes that have not been ever discovered. Volume rendering is an ideal visualization approach for them because it treats all voxels without relying on user-defined surface boundaries. However, these large-scale real-world tomographic data have rarely been volume-rendered at their native resolution, limiting the examination of rich morphological information. Here, we demonstrate a de facto standard volume-rendering pipeline running on a graphical processing unit (GPU)-equipped supercomputing system toward multi-channel, trillion-voxel tomographic data. Our workflow preserves original resolution, capturing detailed morphological information spanning microscopic to macroscopic scales. Systematic comparison of node types shows that GPU memory, rather than host memory, is the primary bottleneck. Our results establish a baseline for large-scale, multi-channel volume rendering of real tomographic data and demonstrate its applicability to geological samples. This work is presented as a practical demonstration of large-scale volume visualization.

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.

Human activity is transforming the shape, size, and resilience of Earths biosphere, degrading and augmenting Holocene baseline conditions at various scales, and replacing the wild biosphere with an anthropogenically modified one. We evaluate episodes of biosphere change throughout Earth history and compare them with contemporary and near-future anthropogenic changes, developing the concept of biosphere disruptors - agents that force global-scale macroevolutionary change. Transient disruptors are short-lived agents (mean 8.0x105 years), including massive volcanism and asteroid impacts. Persistent disruptors, including atmospheric and ocean oxygenation and land plant evolution, remain in the Earth System over long timescales (mean 1.6x108 years). In the geological record, transient disruptors are associated with temporary but sometimes massive biosphere degradation, whereas persistent disruptors are associated with sustained biosphere enhancement. Most anthropogenic biosphere impacts resemble those of past transient disruptors, globally degrading wild biomass and biodiversity. Humanity is driving the second highest rate of biosphere degradation in Earth history after the Cretaceous-Palaeogene bolide impact. However, humanity is the first disrupting agent capable of reflecting on and potentially transforming its impact on planetary habitability. If we can achieve this, humanity could drive the greatest rate of increase in planetary habitability in Earth history on centennial to millennial timescales.

BackgroundLarge Language Models (LLMs) have demonstrated expert-level performance across many medical domains, suggesting potential utility in clinical practice. However, their reliability in the highly specialized domain of moderate hyperthermia (HT) remains unknown. We therefore evaluated the performance of three modern LLMs in answering HT-related questions. MethodsWe conducted an evaluation study by posing 40 open-ended questions--22 clinical and 18 physics-related--to three modern LLMs (DeepSeek-V3, Llama-3.3-70B-Instruct, and GPT-4o). Responses were blinded, randomized, and evaluated by 19 international experts with either a clinical or physics background for quality (5-point Likert scale: 1=very bad, 2=bad, 3=acceptable, 4=good to 5=very good) and for potential harmfulness in clinical decision-making. ResultsA total of 1144 quality evaluation responses were collected. Overall reported mean quality scores were similar across models, with DeepSeek scoring 3.26, Llama 3.18, and GPT-4o 3.07, corresponding to an "acceptable" rating. Across expert evaluations, responses were considered potentially harmful in 17.8% of cases for DeepSeek, 19.3% for Llama, and 15.3% for GPT-4o. Notably, despite "acceptable" mean scores, approximately 25% of responses were rated "bad" to "very bad," and potentially harmful answers occurred in [~]15-19% of evaluations, indicating a non-trivial risk if used without domain expertise. ConclusionOur findings indicate that the performance of LLMs in HT in versions available at the time of investigation is only partially satisfactory. The proportion of poor-quality responses is too high and may lead non-domain experts to misinterpret the available clinical evidence and draw inappropriate clinical conclusions.

Paleobiologists commonly use genera as a proxy for species in biodiversity studies. However, a lingering concern is that patterns among genera might not always faithfully reflect patterns among species. To date, the concern has focused chiefly on measured patterns of richness over time and on implied origination and extinction rates. However, similar issues might arise for studies of morphological disparity. Moreover, there potentially are additional implications of disparity patterns among species versus those among genera concerning the range of observable anatomical characters and whether disparity within genera is comparable to disparity among genera. If clades have some relatively slowly changing characters that workers have used to denote different genera, then we would expect to see congeneric species to cluster in morphospace; however, if such characters are rare, then within-genus disparity might approach among-genus disparity. Here, we use genus-level and species-level disparity patterns among acanthoceratid ammonoids from the Late Cretaceous. In particular, we examine whether these different level imply different evolutionary dynamics over a major ecological event (Ocean Anoxic Event 2) and how disparity within genera (i.e., among congeneric species) compares to disparity among genera. We find genus-level disparity somewhat inflates early acanthoceratid disparity but implies similar patterns over the OAE2. We also find that within-genus disparity is slightly lower than among-genus, but not hugely so. The combined results suggest that acanthoceratoid shell anatomy does not really show "genus" level characters, even if congeneric species do tend to be more similar to each other than to species in other genera. Thus, this might provide more of a warning for other types of studies using anatomical data (e.g., phylogenetic studies) than for disparity studies. Non-technical SummaryMany paleobiologists use genera to examine scientific questions. This leads to questions over whether this broader approach misses important species-level patterns. This study uses acanthoceratid ammonoids from the Late Cretaceous to examine disparity patterns at both the genus-level and the species-level. We specifically examine the disparity at both levels of this group over a time of high stress for this group, Ocean Anoxic Event 2 (OAE2). Our results show that genus-level disparity slightly exaggerates early acanthoceratid disparity but lowers to a similar pattern to the species-level disparity during OAE2. Within-genus disparity is shown to be slightly lower than among-genus, but not enough to be startling. Together, these results indicate that while some species within the same genus tend to be more alike to each other than those in other genera, there isnt a set of true "genus" level characters. This outcome leads to a warning against using anatomical data in phylogenetic studies, but less so for disparity studies.

Cerebrospinal fluid (CSF) is a Newtonian fluid that bathes the brain and spinal cord and oscillates in response to the physiological periodic changes in brain volume, of which the cardiac cycle is a major driver. Understanding this motion is essential for clarifying its contribution to solute transport, waste clearance, and drug delivery. In this work, we study oscillatory and steady streaming flow in the cranial subarachnoid space using a lubrication-based theoretical framework. The model represents the cranial CSF compartment as a thin fluid layer bounded internally by the brain surface and externally by the dura, driven by time-dependent brain surface displacements. We first derive simplified governing equations for flow over an arbitrary smooth sphere-like brain surface and obtain analytical solutions for an idealised spherical geometry with uniform displacements. We then incorporate realistic displacement fields reconstructed from MRI measurements in healthy subjects and solve the reduced equations numerically. The results show that oscillatory forcing produces a steady streaming component that may enhance solute transport compared with diffusion alone. This work provides a mechanistic description of the flow generated by physiological brain motion and highlights the potential presence of steady streaming in cranial subarachnoid fluid dynamics.

Vertebrate gastrulating mesoderm is a prototypic example of a mesenchymal-like tissue undergoing extensive remodelling. While the tissue may be globally represented as a viscoelastic material, the actual biological material is intrinsically complex. To get to a real understanding of its properties, one needs to move to the mesoscale, linking cellular properties to collective phenomena. Vertebrate embryos also display a remarkable variability in mechanical properties, despite which they robustly complete gastrulation. This study attempts to explore these aspects by dissecting Xenopus mesoderm cell behaviour in a minimal system, using aspiration through a microfluidic system to impose controlled stress to a mesoderm aggregate. We show that beyond estimating global rheology at the tissue scale, it is possible to infer a wealth of information based on cell morphology and dynamics. Our data are consistent with collective behaviour being mostly dictated by the balance between the capacity of cells to stretch and the resistance to cell-cell contacts, which limits cell-cell intercalation and thus tissue remodelling. Importantly, tissues are not only able to transmit stress over a distance, they also clearly react to it through actively reinforcing cell-cell mechanical coupling. This adaptative property is found through a broad range of tissue stiffness, and adhesion strength appears to scale with the elastic modulus, suggesting that cell stiffness may ultimately be the key parameter setting mesoderm rheology and accounting for the large differences observed between embryo batches.

ImportanceLung cancer mortality in the United States has fallen substantially in recent decades, yet the relative influence of behavioral, environmental, socioeconomic, and therapeutic factors and their sex specific contributions remains unclear. Understanding these drivers is essential to sustain progress and reduce persistent disparities. ObjectiveTo quantify how behavioral, environmental, socioeconomic, and therapeutic determinants collectively shaped US lung cancer mortality from 1994 to 2020, assess sex specific differences, and forecast mortality trajectories through 2030 using an integrated machine learning framework. Design, Setting, and ParticipantsEcological time series study using publicly available national data from 1994 to 2020. Sex stratified analyses were conducted integrating lung cancer mortality, smoking prevalence, fine particulate matter PM2.5 exposure, Human Development Index HDI, per capita healthcare expenditure, healthcare inflation, insurance coverage, income inequality, and annual drug approvals. ExposuresBehavioral smoking, environmental PM2.5, socioeconomic HDI health expenditure inflation, uninsurance inequality, and therapeutic drug approval indicators. Main Outcomes and MeasuresAge-standardized lung cancer mortality per 100000 population. Temporal changes were modeled using Joinpoint regression. Concurrent associations were assessed using multivariable and elastic net regression, and forecasts were estimated with AutoRegressive Integrated Moving Average models with exogenous variables ARIMAX. ResultsFrom 1994 to 2020, mortality declined by 59 percent in men, from 52.9 to 21.7 per 100000, and by 40 percent in women, from 26.7 to 15.9 per 100000, with faster declines after 2015. Smoking and PM2.5 decreased by more than 45 percent but remained strongly correlated with mortality. In elastic net models, PM2.5 was the strongest predictor for men, while smoking was the strongest predictor for women. Per capita expenditure and HDI ranked higher for men, while uninsurance and income inequality were strong predictors for women. Mortality declines occurred during periods of major approvals of lung cancer drugs. Forecasts suggest continued but slower declines through 2030, with projected rates of 20.2 and 14.9 deaths per 100000 in men and women, respectively. Conclusions and RelevanceSex specific declines in lung cancer mortality reflect different dominant correlates, with air pollution more important in men and smoking more important in women, while socioeconomic conditions and therapeutic advances also influence trends. Continued tobacco control, improved air quality, and equitable access to screening and modern treatment are essential to sustain further reductions in mortality.

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.

Mars relatively moderate surface conditions, availability of solar energy, and in situ resources like water ice, carbon dioxide, and mineral-rich regolith make it a compelling target for supporting life beyond Earth. However, existing experiments testing habitability in Mars conditions generally rely on leachates of physical regolith simulants, which vary in composition across simulant types, leaching conditions, and production batches. We introduce a defined Mars media (DMM) that accurately simulates the biologically relevant nutrients (nitrogen, phosphorus, and sulfur) and stressors (perchlorates, heavy metals) in Martian regolith when it is leached in water at neutral pH. We formulated DMM by combining direct rover and lander measurements from Mars with laboratory measurements of regolith simulant leachates. We validate DMM from a lx to 20x concentrate, equivalent to 40 g/L to 800 g/L of leached regolith. Using DMM with acetate as a Mars atmosphere-derived carbon source, we grew eight heterotrophic bacteria, confirming that organisms can source all essential nutrients from Martian resources. We also show that microbial growth in DMM is robust to uncertainties in Martian regolith composition: sensitivity experiments can identify limiting trace element nutrients and toxins in DMM, and demonstrate that bacterial growth is maintained across at least an order of magnitude variation in their concentrations. This is the first defined Mars regolith media recipe containing both macro- and micro- nutrients, and designed specifically for biological experimentation. By shifting from variable leachate-based approaches to a defined aqueous analog, we enable controlled hypothesis testing of microbial survival, growth, and function. DMM will enable further research on astrobiology, biological in situ resource utilization, large-scale soil remediation, and terraforming. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/719001v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1314b20org.highwire.dtl.DTLVardef@13b57d4org.highwire.dtl.DTLVardef@103315eorg.highwire.dtl.DTLVardef@9e18fe_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Spaceflight-Associated Neuro-ocular Syndrome (SANS) involves complex interactions between intracranial pressure (ICP), intraocular pressure (IOP), and cerebrospinal fluid (CSF) dynamics within the optic nerve subarachnoid space (ONSAS). While existing computational models address specific aspects of these interactions, they lack a comprehensive, system-level representation. To bridge this gap, we present the HEAD (Hemodynamic Eye-brain Associated Dynamics) model. By consistently integrating several previously proposed physiological sub-models, HEAD provides a unified lumped-parameter framework that fully couples cerebrovascular autoregulation, multi-territory ocular hemodynamics, and compartmentalized craniospinal-ONSAS CSF circulation under gravitational loading. This formulation enables the simultaneous analysis of eye-brain-CSF dynamics within a single computational tool. Model predictions were validated against experimental data from supine (0{degrees}) to head-down tilt (HDT, -30{degrees}) postures, accurately reproducing posture-dependent IOP increases and achieving an excellent ICP match against clinical benchmarks at the -6{degrees} HDT standard bed-rest angle. The coupled system predicts bed-specific ocular hemodynamic responses, with retinal blood flow exhibiting the largest relative increase under HDT compared to the ciliary and choroidal circulations. Crucially, explicitly modeling the ONSAS as a distinct compartment reveals a posture-dependent pressure drop of 1.89-3.69 mmHg between the intracranial and perioptic spaces. This compartmentalization yields a translaminar pressure profile that remains positive (8.05-11.83 mmHg) across all simulated conditions but is chronically reduced under sustained HDT. Ultimately, the HEAD model elucidates the physiological mechanisms linking gravitational stress to translaminar mechanics, providing a robust computational foundation to investigate SANS and supply boundary conditions for structural models of the optic nerve head.

AO_SCPLOWBSTRACTC_SCPLOWBiosafety risk assessment traditionally relies on categorical scales embodied by the four WHO Risk Groups and biocontainment levels. Mapping such categories to quantitative metrics is an open problem for the field: the classifications are too coarse for operational decision-making, yet strictly probabilistic language remains inaccessible to most safety professionals, laboratory managers, and decision-makers. To bridge these gaps, the present work develops a quantitative Bayesian framework for laboratory risk management that combines WHO Risk Group classification as a prior with a Markov chain model of the incident-disaster escalation chain. Risk is reported on a log-risk scale that transforms multiplicative probabilities into additive quantities, mirroring the decibel scale in acoustics. The framework accommodates longitudinal updating with local incident data and quantifies the separate contributions of training, preventive maintenance, and inspection to system-level safety. Resource allocation recommendations are derived that complement existing compliance frameworks with auditable, evidence-based prioritisation. The framework is illustrated on synthetic BSL-3 scenarios and shifts the perspective of biorisk governance from static compliance assessment to dynamic risk and resource management.

The Bayesian Ordered Lattice Design (BOLD) method for Phase I clinical trials is extended to address an important challenge. It is widely understood that conventional Phase I trial designs are not consistently effective in determining safe and active dose levels. The US FDA launched the Project Optimus, aimed at reforming the paradigms of dose optimization and selection. We propose a backfill BOLD design (BF-BOLD) that centers on BOLD for dose-finding but also adds an activity evaluation for each patient. Our method for determining the optimal biological dose (OBD) first involves identifying the maximum tolerated dose (MTD) and then assessing activity rates among dose levels below the identified MTD. This approach is straightforward and does not require complex statistical modeling. The results of the simulation indicate that performing dose-finding trials with backfilling can both enhance safety and activity assessment, thereby improving treatment sustainability while also preserving the potential for efficacy of the Recommended Phase II Dose (RP2D). We also demonstrate the applicability of the backfill design for reducing overdose rates, and as a more attractive alternative to small-scale dose expansion trials that follow dose escalation. Backfill designs are an important design approach for early phase trials.

The deepest phylogenetic divergence within crown birds (Neornithes) is that between the reciprocally monophyletic Palaeognathae and Neognathae. Extant palaeognath diversity comprises the iconic flightless "ratites" (ostriches, rhea, kiwi, cassowaries, and emu), as well as 46 species of volant tinamous in Central and South America (Billerman et al., 2020). Although the earliest stages of palaeognath evolution remain shrouded in mystery due to a sparse fossil record, a group of apparently volant extinct palaeognaths from the Paleogene of Europe and North America, the lithornithids, can help to clarify palaeognath origins. Here, we use high resolution microCT scanning to characterize the morphology of two lithornithid specimens from the early Eocene (Ypresian) London Clay Formation: the neotype of Lithornis vulturinus (NHMUK A5204), from the Isle of Sheppey, Kent, England, and a newly discovered clay nodule containing lithornithid postcranial remains from the nearby locality of Seasalter. This three-dimensional dataset reveals bones from the L. vulturinus neotype that are partially or completely covered by matrix, allowing us to redescribe this critical specimen in new detail and present a revised differential diagnosis of L. vulturinus. We refer the new specimen from Seasalter to L. vulturinus on the basis of apomorphies such as a proximally directed lateral process of the coracoid, caudally divergent lateral margins of the sternum, an arcuate deltopectoral crest, as well as its provenance from a nearby penecontemporaneous locality. The Seasalter specimen contains abundant postcranial material that provides new insight into bones damaged or missing in the neotype, including two undamaged scapulae bearing the hooked acromion that is a diagnostic feature of lithornithids, two complete coracoids, and a nearly complete three-dimensionally preserved sternum. Its estimated body mass is one third larger than that of the neotype, indicating intraspecific variation within L. vulturinus that may reflect sexual dimorphism. Molecular divergence dates and Cretaceous neognath fossils indicate the presence of total-clade palaeognaths before the K-Pg mass extinction event; detailed anatomical descriptions of Paleogene palaeognaths will assist in the identification of the first total-clade palaeognaths from the Cretaceous, and provide insight into how and when flight was independently lost among Cenozoic crown palaeognaths.