Interface Focus

Recovering well-preserved vertebrate remains from underwater caves has provided critical insights into archaeological and palaeontological records worldwide. However, understanding how bone assemblages form and are modified in underwater environments remains limited due to stable low energy burial conditions that produce time-averaged deposits, and underwater settings that hinder traditional recording and recovery methods. This study applies an actualistic taphonomic framework to three assemblages of domesticate animal bones (N = 231) from two underwater caves, Green Waterhole and Gouldens Sinkhole, near Mount Gambier, South Australia, encompassing known submerged (wet; N = 134) and dry (N = 97) burial conditions. The assemblages were examined to assess how wet and dry cave environments impact bone distribution, surface and microstructural modification. Radiocarbon dating of 41 specimens indicates that domesticate fauna were deposited over decadal and centennial timescales, allowing taphonomic signatures to be contextualised through time. Statistically significant differences were identified between wet and dry burial contexts. Bones recovered from wet contexts exhibit mostly better preservation, including skeletal elemental completeness, surface, and microstructure, than those from dry caves. However, some of the submerged specimens also have elevated frequencies of bone surface corrosion with macroscopic evidence for heterogenous black biological staining, algal or biofilm attack, and a distinctive form of circular etching. Histotaphonomy further reveals patterns of peripheral cyanobacterial tunnelling across most bones recovered from submerged contexts. Bones from dry environments were dominated by terrestrially linked tunnelling across all regions of the bone cortex. These findings can be explained by variation in light availability across different cave zones which influences biological activity and, in turn, the expression of taphonomic markers on bone externally and at the microstructural level. This is the first study to provide a benchmark bone dataset for reconstructing depositional histories and post-depositional reworking in underwater cave environments under a taphonomic framework.

Despite receiving significant interest from the biological and engineering communities, several questions about the underlying reasons for the form of the deep-sea sponge Venus Flower Basket (Euplectela aspergillum) remain unanswered. In particular, the basis for the sequence of emergence of three distinct macroscopic geometric features, while speculated upon, has not been validated. These features are (i) an interwoven cross-grid in the juvenile stage, (ii) a diagonal weave atop this initial grid, and (iii) a helical ridge that emerges in the mature phase of the sponge. This work uses computational design and additive manufacturing to fabricate models of each of these phases in sequence and subjects the models to mechanical tests in compression, bending and torsion. The results show that each feature has a singular advantage, even after accounting for the increase in mass associated with their addition: increased compliance for interwoven cross-grid, increased bending stiffness for diagonal weaves, and improved torsional rigidity for the helical ridge. The work postulates that the prioritization of compliance in the juvenile phase and a transition to a stiffer structure in the mature phase is a strategy that enables the sponge to avoid high internal stresses to avoid failure in its inherently brittle, silica-derived architecture. Highlights1. The benefits of three macrostructural design elements of the Venus Flower Basket (E. aspergillum) that emerge sequentially in its development are examined: (i) the interweaving cross-grid, (ii) the diagonal weave that overlays this cross-grid, and (iii) the helical ridge reinforcement that forms over this diagonal weave. 2. For the first time, this work models each of these design features sequentially and studies their mechanical benefits through an experimental exploration of the behavior of idealized geometries in three test domains: compression, bending, and torsion. 3. Results show that each of these three structural elements has a unique functional advantage depending on the organisms growth stage, even after accounting for differences in mass: interweaving enables higher compliance under compression, the diagonal weave improves bending stiffness, and the helical ridge improves torsional stiffness.

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.

Walruses have long played a vital role in Arctic subsistence and commercial economies, yet accurate radiocarbon dating of walrus-derived archaeological materials is complicated by regional variation in marine reservoir effects. This study presents new {Delta}R values derived from 31 new and 15 legacy radiocarbon dates on known-age walrus specimens spanning multiple populations of Odobenus rosmarus rosmarus and O. r. divergens. The results reveal substantial inter-population variability, with {Delta}R values ranging from -140 (Franz Josef Land) to +295 (Pacific), underscoring the importance of population-specific calibration. A pooled {Delta}R of +17{+/-}12 was calculated for western Greenland and the Canadian Arctic--regions central to the medieval Norse ivory trade--and applied to 12 walrus rostra and ivory artefacts excavated in Trondheim, Norway. The resulting calibrated dates are from the 11th to early 14th centuries CE, confirming that seemingly anomalous post-15th-century finds are residual rather than evidence of continued trade. An alternative {Delta}R estimate of -110{+/-}35, derived solely from archaeological context dates, suggests potential time lags between harvest and deposition. These findings demonstrate the value of known-age walrus {Delta}R data for refining chronologies of Arctic exploitation and long-distance trade, while highlighting the need for provenance studies in archaeological dating and both the pertinence and limitations of mollusk-based reservoir corrections.

Trilobites had biramous appendages with an inner endopodite (walking leg) and outer exopodite (gill) connected to the body through the protopodite (limb base). Whereas both endopodite and protopodite were involved in both locomotion and feeding, the exopodite has been subject to various functional interpretations including respiration, ventilation and swimming. Evidence from sites with exceptional fossil preservation indicate that trilobite exopodites show substantial variability in terms of the number and size of their articles, lamellae and setae, but the implications of this morphological diversity have never been investigated. Here, we created anatomically correct 3D models of exopodites in O. serratus and T. eatoni to calculate the SA of the lamellae and explore its relationship with body size. Our results indicate a large SA for O. serratus at 16,589 mm2 compared to the 2,159 mm2 for the much smaller T. eatoni. We also calculated lamellar SA for nine additional trilobite species with exceptionally well-preserved appendages based on lamellar measurements. The results indicate that lamellae SA of trilobites increased exponentially with overall body size. Trilobite data follows the same trendline of gill SA/biomass observed in extant species and thus supports the interpretation of their exopodites as respiratory structures despite substantial variation in morphology.

Cerebrospinal fluid (CSF) circulates around and through the brain, supporting neural homeostasis by regulating the extracellular chemical environment. Yet the physical mechanisms governing CSF-driven solute transport remain poorly understood, limiting the design of diagnostic and therapeutic strategies targeting brain clearance and drug delivery. Pulsatile CSF flow in the cranial subarachnoid space (cSAS), is driven by cardiac, respiratory, and sleep-related vasomotion. Over longer timescales weaker steady flows, such as inertial steady streaming, Stokes drift, and production-drainage flow, may contribute to solute transport, but their role and relative importance remain unclear. Here, we develop a simplified two-dimensional model of CSF flow and solute transport in the cSAS using lubrication theory. Through multiple-timescale and asymptotic analyses, we derive a reduced long-time transport equation in which advection is governed by the Lagrangian mean velocity, incorporating steady streaming, production-drainage flow, and Stokes drift. Analysing three physiologically relevant case studies, we show that steady flows can substantially reshape concentration profiles, enhance dispersion, and alter clearance efficiency. Our results clarify the mechanisms underlying CSF-mediated transport, predict distinct regimes in humans and mice, and highlight the importance of subject-specific physiological parameters when interpreting contrast-agent and intrathecal drug-delivery studies.

As we know, bacterial motion navigates complex environments in natural settings, providing a basis for scientific study to understand their dynamics. Hydrodynamics drive wall alignment and accumulation, but research remains unclear about the extent to which confinement alone, without hydrodynamics, modulates bacterial dynamics. Therefore, we develop a 2D model to study the effects of isolated steric and hydrodynamic forces on bacterial motion in 10- and 50-m microchannels. We used bacteria to model self-propelled rigid rods with run-and-tumble motion, and then compared dry systems (purely steric wall effects) with wet systems (hydrodynamic effects). We found that bacterial speeds and their orientation are independent of channel dimensions in dry systems. In wet systems, we observed strong wall-hugging and alignment due to wall-induced hydrodynamic interactions, enhanced residence times, and a slight increase in the observable effective speed in 10 m microchannels compared to 50 m channels (where bacteria-maintained bulk-like dynamics). Our study thus emphasises when confinement affects bacterial motion solely due to pure dry geometry, and when hydrodynamics play an essential role. This study provides a template for microfluidics-based experimental prediction of bacterial dynamics and could be applied to an analysis of antibiotic resistance. Significance StatementMicrobes rely on self-propelled motion to navigate complex environmental systems and survive and persist. In such an environment, physical interactions with surrounding boundaries play a key role in how bacteria move, orient, or settle in complex spaces. Although many studies show that hydrodynamic forces near walls influence how bacteria swim in liquid, there is still confusion about whether confinement, with or without hydrodynamic effects, can change the complete bacterial pattern. Our 2D model shows that confinement (purely steric wall effects, dry limit) alone does not alter bacterial motion patterns. It is the hydrodynamics (wet limit) that drives bacteria to orient toward walls, remain near boundaries, and direct motion in narrow channels. This work clarifies when confinement and hydrodynamics actually affect bacterias motion and provides a practical way to understand bacterial behaviour in biological systems, including in microfluidic applications and studies of antibiotic resistance strategies.

Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks--hallucination and automation bias--that threaten the fidelity of AI-assisted learning. We present Onco-Shikshak V7, an AI-native adaptive learning platform that addresses both challenges through a unified cognitive architecture grounded in learning science. The system replaces isolated educational modules with four authentic clinical workflows--Morning Report, Tumor Board, Clinic Day, and AI Textbook--each scaffolded by a nine-module pedagogy engine that integrates ACT-R activation dynamics (illness scripts), Item Response Theory (adaptive difficulty), the Free Spaced Repetition Scheduler (FSRS v4), Zone of Proximal Development (scaffolding), and metacognitive calibration training (Brier score). Six specialist AI agents--medical oncology, radiation oncology, surgical oncology, pathology, radiology, and oncology navigation--engage in multi-disciplinary deliberation with per-specialty retrieval-augmented generation (RAG) grounding across nine authoritative guideline sources including NCCN, ESMO, and ASTRO. The platform provides 18 clinical cases with decision trees across six cancer types, maps every interaction to 13 ACGME Hematology-Oncology milestones, and implements four closed-loop feedback mechanisms that connect session errors to targeted flashcards, weak domains to suggested cases, and all interactions to a persistent learner profile. Technical validation confirms algorithmic correctness across eight subsystems. To our knowledge, this is the first system to unify ACT-R, IRT, FSRS, ZPD, and metacognitive calibration in a single medical education platform. Formal learner evaluation via randomized controlled trial is planned.

This work presents the development of a novel approach to model the dynamics of cancer cells in microcirculation. We investigate the role the membrane elasticity, and cancer cell shape on deformation dynamics under the shear and pressure forces in a micro-channel. The proposed numerical model is based on a hybrid continuum-particle approach. The cancer cell model includes the cell membrane, nucleus, cytoplasm and the cytoskeleton. The Dissipative Particle Dynamics method was employed to simulate the mechanical components. The blood plasma is modeled as a Newtonian incompressible fluid. A Fluid-Structure Interaction coupling, leveraging the Immersed Boundary Method is developed to simulate the cells response to flow dynamics. We quantify how subtle variations in these biophysical properties alter deformation indices such as sphericity and aspect ratio, and stress distributions on the membrane of the cancer cell. Our findings align well with existing computational and experimental studies. Results reveal that increased membrane stiffness reduces overall deformation as well as the total distance traveled. Similarly, cell geometry strongly influences flow-structure interactions: near-spherical morphologies exhibit stable deformation with minimal sensitivity to shear variations, whereas elongated geometries show pronounced orientation and stretching effects. Collectively, these findings highlight the critical importance of cell-specific heterogeneity in governing cell dynamics in microvascular flows. Furthermore, the intracellular and extracellular dynamics response of the cancer cell are intrinsically linked to their shape, in which certain morphologies displayed strong resistance to the fluid-induced forces and the ability to migrate in various directions. The insights obtained provide a mechanistic framework for understanding circulating tumor cell transport in shear-dominated environments during metastasis. Our work may inform the design of biomimetic microfluidic systems and therapeutic strategies targeting cancer cell detection and cancer prognosis.

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.

Early detection is critical for lung cancer patients. One lung cancer detection method under study is using sniffer dogs. This study aimed to evaluate, retrospectively, the sensitivity and specificity of the Cancer Detection Dog Collective (CDDC(R)) method under training conditions. A team of five trained sniffer dogs analyzed breath samples from lung cancer patients and cancer-free volunteers, and a cancer sample is positive if at least three dogs indicate it. Dog handlers and experimental observers were blinded to sample identity, and detection accuracy was assessed. Primary endpoint was sensitivity, and selectivity and confounding factors were also assessed. Samples were collected in 2024 from 824 volunteers, including 111 with a confirmed diagnosis of lung cancer (mean age 60, range 34-80, 18% early-stage cancer, 46% not yet oncological treated). A total of 11,900 breath samples were tested with 125 test runs per dog. Individually, the five dogs demonstrated detection performance with sensitivities between 82% and 89%, and specificities of over 95%. The CDDC(R) dog teams corporate decision revealed a sensitivity over 95% and the rate of false positives was 0%. Analysis of potential confounding factors revealed that weather conditions and supervisor skills were associated with the dogs performance. The CDDC(R) method showed high consistency in training scenarios. Further studies should evaluate this method in a controlled clinical study alongside lung cancer screening.

Coral, as a bioreactor, has to continuously interact with surrounding environment to maintain a healthy state. A multi-physics reaction engineering model has been developed to capture this interaction. The coral interior is modeled as interconnected reaction units respectively for photosynthesis, respiration, and calcification, whose reaction kinetics are influenced by environmental fluctuations. Coupling between coral and environment is realized by bi-directional mass transfer at the coral-seawater interface, with consideration of the unique flow fields induced by ciliary beating. By resorting to this comprehensive model, we discover that ciliary beating demonstrates distinctively different diurnal and nocturnal functions. During daytime, beating can help reduce photosynthetic oxygen accumulation to prevent hyperoxia-induced mortality, while enhancing carbon dioxide uptake efficiency to promote nutrient production. At night, however, beating promotes oxygen acquisition for adequate respiration, while expelling carbon dioxide to inhibit symbiotic destruction under acidic stress. The model further enables mechanistic analysis of the detrimental impact of climate change on coral health, where the influences from two key factors (i.e., temperature and CO2 level) can be decoupled. Its interesting to find out that the elevated temperature plays a dominant role during daytime, while at night the coral is dominantly influenced by rising CO2 level.

Modern sponges (Porifera) diverged by the Cryogenian, but their silicious skeletons do not appear in the fossil record until one hundred million years later, a time-span termed the "spicule gap" and thought to be a taphonomic artifact even though sponges convergently evolved siliceous spicules. Due to sponges position in animal phylogeny and important role in regulating ocean chemistry, the timing of their biomineralization has major implications for the changing tempo and mode of Earth systems as animals radiate. In a comprehensive dataset of Ediacaran and Cambrian sponges, we find that spicules are readily preserved in Cambrian environments more extreme than those of the Ediacaran. Given the convergent evolution of siliceous spicules, we find that the fossil record accurately represents when spicules first evolved in the different sponge lineages.

Despite much of the literature perceiving fish schooling as an organized system with a focus on fixed formations for theoretical analyses, experimental observations suggest that frequent positional rearrangement commonly occurs. Previous studies have also demonstrated that fish schools reduce locomotor costs relative to individuals swimming alone. This introduces an intriguing dichotomy. How can individual fish within schools exhibit dynamic interactions while also saving energy? We hypothesize that schooling dynamics are the result of positional and kinematic modulation of individuals responding to fluid dynamic stimuli from the movement of neighbouring individuals. We propose a two-tier approach to studying kinematic modulation within fish schools. First, quantification of the variation of individual movement in a school relative to that of a solitary individual uses an analytical pipeline combining artificial-intelligence-enabled tracking and video processing. Second, the study of kinematic modulation in response to hydrodynamic stimuli uses a mechanical flapping mechanism coupled with an enclosure to control fish position. We discovered that fish in schools exhibit higher levels of positional and kinematic modulation than individuals swimming alone. Fish swimming in enclosures can robustly respond to fluid stimuli from either a simple robotic fish or other fish located in proximity. This two-tier approach allows high-resolution analysis of positional and kinematic modulation within fish schools and their impacts on energy conservation resulting from collective movement.

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.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.