Accurate assessment of bone mineral density (BMD), particularly volumetric BMD (vBMD), is central to evaluating bone quality and monitoring bone regeneration. Although dual-energy computed tomography (DECT) combined with virtual monoenergetic imaging (VMI) enhances quantitative imaging, measurements based on single-energy information remain limited by sensitivity and stability. Here, a rabbit tibial defect model was established, with micro-computed tomography used as the reference standard for vBMD. DECT-derived VMIs at low, medium and high energy levels were analysed to extract five quantitative indicators, including three single-energy values, an energy ratio and an energy-difference metric. Quantitative performance was systematically compared using linear regression, cross-validation, prediction-error analysis and Bland-Altman evaluation. The energy-difference metric showed performance comparable to that of the optimal single-energy indicator (VMI40), with marginally better model fit and agreement limits. These results suggest that integrating low- and high-energy information through an energy-difference approach provides a physically interpretable and practical framework for quantitative assessment of bone tissue. However, as this study was conducted in a rabbit model, future human cohort studies are required to confirm its clinical utility.
Human postural control during quiet standing has often been modelled as a lightly damped dynamical system in which low-frequency modes contribute to sway behaviour. Reduced effective damping may increase amplification of these modes, potentially contributing to instability. In engineered systems, resonance amplification is commonly mitigated through frequency-selective energy redistribution using tuned mass dampers, raising the question of whether analogous principles apply to biological control systems. Here, we develop a theoretical framework linking classical vibration absorption to a reduced-order model of human postural dynamics, showing how passive mass coupling reshapes the frequency response of the system. The virtual tuned mass damper reduced compliance near the dominant low-frequency mode without globally suppressing motion. For moderate intrinsic damping (ζh = 0.15), peak response amplitude was reduced by approximately 20%. Effectiveness increased as intrinsic damping decreased, with optimized peak compliance reductions of 52% at ζh = 0.05 compared with 12% at ζh = 0.25. Compliance landscapes exhibited a well-defined optimal basin, indicating robust parameter selection. Sensitivity analyses revealed a nonlinear relationship between intrinsic damping and attenuation effectiveness. Together, these findings demonstrate that passive mass coupling can selectively redistribute low-frequency sway energy within a simplified balance model and provide a mechanistic basis for investigating resonance-targeted approaches to wearable balance assistance.
Unlike viral capsids, which protect and transport genetic material, non-viral protein shells perform diverse biological functions, including enzymatic catalysis, iron storage and protein chaperone activity. These shells often exhibit tetrahedral (T) and octahedral (O) point group symmetries, and their structures can include four- and even eightfold capsomers that are absent in viral capsids. Here, to rationalize the design of such nanocages, we develop a phenomenological theory based on the approach of critical density waves (DWs). We consider these spherical proteinaceous structures to be the result of condensation of one or two irreducible spherical DWs with close wavenumbers and place individual proteins (or capsomers) at the maxima of interference patterns generated by these waves. By examining the structures presented in the protein data bank (PDB), we identify 22 structural types of shells well rationalized within the theory and discuss the principles controlling protein order. In particular, we explain why these assemblies often possess local quasicrystalline order and predict the structures of yet unknown shells with cubic symmetries. The results obtained offer valuable insights into the rational design of stable, functional protein nanocages.
Crises such as starvation pose a serious threat to microbial populations, prompting cells to adopt survival strategies, such as cooperation or competition. Although cooperation among clones is common, recent studies have shown that yeast cells can kill clonal cells under glucose depletion by secreting autotoxins. Adapted cells survive, whereas non-adapted latecomers are eliminated. Remarkably, this toxin-adaptation (TA) system, which uses the same set of autotoxins, is conserved across distantly related yeast species. This is puzzling because conventional toxin-immunity (TI) systems are prone to exploitation by 'cheaters', cells that benefit from immunity without producing toxins, and typically diverge in an evolutionary arms race. To investigate how this system is maintained, we analysed its evolutionary stability using population dynamics modelling. The system does not evolve in constant environments: cheaters outcompete adaptive cells during continuous starvation, while sensitive cells that produce neither toxin nor immunity dominate in continuous nutrient-rich conditions. However, when the environment switches stochastically between starvation and nutrient-rich phases, with short starvation periods and long nutrient-rich periods, the TA system becomes evolutionarily stable. These findings suggest that fluctuating environments can promote the emergence and long-term maintenance of the TA system, highlighting the critical role of environmental switching in shaping microbial survival strategies.
The self-organization of asynchronous rhythms into synchronized beating in in vitro cardiomyocyte networks is a fundamental problem in systems biology research. Inspired by the dynamics of chemical oscillators, this paper presents a programmable model framework based on a discretized Belousov-Zhabotinsky reaction-diffusion system to study signal propagation and synchronization in cardiomyocyte networks. We propose a hybrid discrete-continuous geometry in which active excitable units (simulating cells) are embedded in a passive diffusive medium, with the dynamics of each unit governed by the Rovinsky-Zhabotinsky equations. Unlike conventional continuous media models, the present framework represents each cell as an independently programmable discrete unit, thereby explicitly capturing the discreteness and cell-to-cell variability inherent in in vitro cardiomyocyte networks. We systematically compare model simulations with in vitro experiments on neonatal rat cardiomyocyte networks using calcium imaging and mechanical stimulation. The framework reproduces multi-scale dynamical features: (i) intracellular excitation-propagation-recovery cycles, (ii) topology-dependent signal transmission in both regular and irregular networks, and (iii) the self-organized transition from cell-to-cell synchronization to global population synchronization. These qualitative agreements demonstrate that a simplified, programmable reaction-diffusion framework can capture the signalling dynamics of cardiomyocyte excitable systems, offering a new perspective for investigating signal coordination in cardiomyocyte networks.
The possible relationship between diet and geographical locality is important for understanding the putative effects of climate change on animal behaviour and survival. Narwhals have two maxillary teeth, one of which may become a tusk. In these teeth, various chemical elements are incorporated and stored at the time of tooth formation. Here, we use inductively coupled plasma optical emission spectroscopy to investigate 12 chemical elements in tooth powder drilled from a total of 197 narwhal teeth from both sexes, tusks, embedded teeth, dentin, cementum and different geographical localities around Greenland. Additionally, the spatial distribution of Sr and Zn was mapped on a slice of tusk using X-ray fluorescence. Quantifications of Ca and P showed a mean degree of mineralization of 0.565 g g-1 and their relationship enabled the determination of the presence of approximately 1 wt% non-biomineral-associated P, which must be associated with biomolecules. While several differences were found across sampling parameters, no significant differences were found between males and females for any elements. Finally, the concentration of Cl, Ba, K and S depended on geographical locality; geographical locality was the only significant factor for Cl. These differences could potentially be linked to the circulation of freshwater from melting ice at and around Greenland.
In an emerging pandemic, policymakers need to make decisions with limited information and require trade-offs between the health impact of the pandemic and the economic costs of the response. Most mathematical models have focused on direct health impacts, neglecting the economic costs of control measures. Here, we introduce a framework that captures both health and economic costs and compare the expected aggregate costs of alternative strategies across a range of epidemiological and economic parameters. We find that for diseases with low severity, mitigation tends to be the most cost-effective option. For more severe diseases, suppression tends to be most cost-effective if the basic reproduction number R0 is relatively low, while elimination tends to be more cost-effective if R0 is high. We use the example of New Zealand's response to the COVID-19 pandemic in 2020 to anchor our framework to a real-world case study. We find that parameter estimates for COVID-19 in New Zealand put it close to or above the threshold at which elimination becomes more cost-effective than mitigation. We conclude that our proposed framework holds promise as a decision-support tool for pandemic threats, with further work needed to account for population heterogeneity and other factors relevant to decision-making.
Viral particles, or virions, may remain infectious in the dry residue of respiratory droplets for times as long as hours. This is counterintuitive, since salt concentration increases dramatically as the drop's water evaporates, making the drop a harsh environment for virions. It has been hypothesized that the drop components (mainly salt and the glycoprotein mucin) segregate during the evaporation process, with virions being transported away from salt deposits. This would protect them from the salt's damaging effects. Thus, understanding where virions reside in a drop residue is essential to disentangle the physico-chemical mechanisms that drive their inactivation. However, determining the virion location in an environment as heterogeneous and complex as a respiratory drop is challenging. Here, we show experimentally that virions in a drop's dry residue are found mainly forming aggregates in protein-rich regions, outside salt crystals. The mechanism yielding the observed viral spatial distribution can be found in the internal flow phenomena within the droplet, which are described using numerical simulations. We anticipate our results to be relevant to explain the discrepancies in the infectivity decay rates measured in respiratory drops in previously reported studies.
Rose prickles are vegetal weapons familiar to most green-thumbed individuals. Their distinctive feature is an oval cross-section, which contrasts with the mostly circular shapes of natural and man-made stingers. However, the reason for their eccentricity remains poorly understood. We explore the hypothesis that the morphology of prickles is physically limited by trade-offs between bending, buckling and cutting. In our experiments, polydimethylsiloxane stingers with varying cross-sectional shapes were used to lacerate a gelatin skin analogue. Circular stingers bend along the direction of travel and create superficial scratches. By contrast, prickles with a moderate aspect ratio have enough mechanical stability to cut (or provide grip). However, when they become too narrow, the blades weaken and buckle. The least unfavourable aspect ratio falls between 2 and 4, not inconsistent with the available biological data (2.7±0.7). This novel mechanical trade-off suggests that cutting mechanics are a potential driver of the rose prickle morphology.
Position-matching is a common task used to assess the sense of position, a sub-modality of proprioception. The ‘sensible senseless person’ (SSP), a theoretical construct that lacks sensory perception but is capable of logical inference, was developed to critically evaluate common outcome measures in such tasks. This framework reveals that traditional measures are influenced by task structure, strategic responses and the prior beliefs of the SSP. To address these limitations, we propose the Guess Index (GI), an outcome measure designed to ensure that the SSP receives the same score regardless of task or personal parameters. To demonstrate the GI's utility, we applied a simplified, clinically oriented position-matching task to healthy individuals and persons with stroke. Participants' performance often followed the SSP-predicted pattern. The GI correlated with traditional outcome measures, but was more sensitive to group differences and the only metric that classified persons with stroke above chance level in this dataset. Future studies should extend this framework to other proprioceptive sub-modalities and larger, more diverse populations before the GI can be considered a general index of proprioceptive adequacy with robust clinical cutoffs.
Global fisheries are under increasing pressure from overfishing and climate change, threatening marine biodiversity and food security. Disentangling the impacts of these two drivers is essential for sustainable management, yet it remains a major challenge due to the complex nature of marine ecosystems. Here, we utilize a data-driven empirical dynamic modelling framework to assess causal relationships between harvest rate, sea surface temperature (SST), and the productivity of 155 fish stocks worldwide. Our analysis identifies fishing pressure as the most prevalent driver of stock productivity, with a causal link detected in 71 stocks (45.8%). SST was also identified as a significant driver of productivity in 39 stocks (25.2%). While the impact of harvest rate was stock-specific, being either positive or negative, the influence of SST was predominantly negative and, in many cases, intensified over time. These results provide empirical evidence that direct exploitation and climate warming are systematically impacting fish stock productivity, with direct exploitation emerging as the predominant driver across stocks. This predominance suggests that improved harvest management could help buffer or mitigate some of the negative impacts of climate warming on fisheries productivity.
The iris is a deformable circular diaphragm that regulates pupil size in response to changes in illumination through the antagonistic actions of sphincter and dilator muscles. While the phenomenological relationship between pupil size and light intensity is well studied, the mechanical interplay between active muscle contraction and passive iris tissue remains poorly understood. In this study, we develop a finite-element model of the human iris using an active strain formulation to investigate the mechanics underlying pupil regulation under physiological conditions. The iris is represented as a fibre-reinforced soft tissue, with passive matrix behaviour modelled as isotropic, nonlinear elastic and active muscle contraction introduced via contractive strains along fibre directions. Numerical simulations are performed using a dedicated finite-element code. By progressively including active and passive tissue components, we analyse how tissue architecture affects pupil kinematics, stress distribution and interaction with supporting boundaries at the iris root. Results reveal a counterintuitive yet significant role of passive tissues in shaping three-dimensional iris deformation and moderating boundary reactions. This computational framework provides a mechanically consistent basis for understanding iris biomechanics and can support future studies extending to more complex physiological or pathological conditions.
Bioreactors are essential tools in tissue engineering and advanced cell culture because they provide dynamic environments that support cellular growth, organization and tissue maturation. However, most existing systems lack integrated real-time monitoring and feedback control, limiting their reproducibility, scalability and translational relevance. Here, we report the development of a modular, continuous-flow rotational (CFR) bioreactor system equipped with multi-sensor feedback to enable real-time monitoring and control of pH, temperature, dissolved oxygen, CO2 and rotation speed. A key design feature is a swivel connector architecture that enables uninterrupted media and gas exchange during chamber rotation while maintaining sterility and preventing tubing entanglement. Constructed from off-the-shelf components with custom firmware and a Python-based interface, the system maintains stable and physiologically relevant culture conditions. Its modular design supports both macro- and microscale formats and enables multiplexed experiments. The system's versatility was demonstrated by culturing U-87 MG glioblastoma cells on chitosan scaffolds and Jurkat T cells in suspension, resulting in improved cell viability and distribution compared with static controls. These results demonstrate that the CFR platform provides a scalable, sensor-integrated culture system for applications in tissue engineering, disease modelling, drug screening and regenerative medicine.
When making a decision, individuals use their private information to evaluate the available options. In a group, they can also learn from other group members' choices, which partially reveal their own private information. Bayesian estimation provides tools for modelling decisions that optimally balance these two sources of information and can incorporate empirically documented cognitive biases, such as the conformity bias, which promotes consensus at the expense of accuracy. We use this framework to analyse a situation in which an uninformed individual judges the accuracy of a group of informed individuals-a situation relevant in human societies, where citizens routinely follow recommendations provided by groups of experts. In the absence of conformity bias, group accuracy increases monotonically with agreement level, suggesting that unanimity is a reliable indicator of trustworthiness. However, a more realistic model that describes groups with different degrees of conformity shows that unanimity is not an indicator of high reliability. Instead, the most reliable groups are those with relatively high levels of agreement, but not reaching total consensus. This result shows that moderate levels of dissent are not only useful as an error-correcting mechanism but are also a characteristic of trustworthy groups.
Inflammation plays an important role in the pathological remodelling and rupture of intracranial aneurysms. One of the challenges in predicting the prognosis of intracranial aneurysms lies in the variation of the inflammatory state in aneurysm tissues from patient to patient. The connection between aneurysm biophysical environment and inflammation is largely unexplored by predictive computational models. In this study, a computational model based on a positive feedback mechanism driven by paracrine signalling between monocytes, macrophages and soluble inflammatory factors was proposed. Based on this model, three inflammatory factor transport regimes characterized by the dominance of (i) diffusion, (ii) production, or (iii) removal that drove at distinctive inflammatory dynamics were identified. A dimensionless group, production-to-transport ratio, strongly correlated with the simulated macrophage count (R2=0.97). The theoretical model in this work was simplified, but it demonstrated that inflammatory factor transport could have a profound impact on the inflammation process. This study suggests that haemodynamics may indirectly influence the state of inflammation, beyond the well-documented mechanosensory response of the vascular endothelium, by modulating the transport conditions for inflammatory factors.
Understanding causality is central to scientific discovery, importantly enabling the elucidation of mechanisms within complex systems. However, current methods of causal inference face significant challenges in uncovering interventional causal interactions within non-interventional systems, in particular under latent confounders, as they either focus solely on associations or require additional interventional manipulation. To overcome these limitations, here we introduce a novel method of Interventional Dynamical Causality under Invisible/Latent confounders (named ICIC or IC2) to decipher interventional dynamical causality based solely on non-interventional data even under latent confounders. IC2 is theoretically grounded in the dual orthogonal decomposition theorem in the delay embedding space and is computationally implemented with the constructed interventional data from observed non-interventional data by deep neural networks. Comprehensive benchmarking demonstrates that IC2 outperforms alternative methods in recovering causal structures in various biological applications. In particular, IC2 was not only validated by true interventional effects with knockout experiments, but also reconstructed biological networks from real-world data, and predicted the perturbation effects of single-cell CRISPR perturbation experiments. These results show the power of IC2 in estimating interventional effects where experimental intervention is not feasible.
Direction finding in migratory birds is known to be affected by oscillating magnetic fields (OMFs) in the radiofrequency range. This experimental fact was earlier interpreted in terms of the direct influence of OMFs on electron spins in the molecule of cryptochrome that, according to the photochemical model of magnetoreception, serves as the primary sensor of the geomagnetic field. In our experiments, birds (pied flycatchers) were subjected to OMFs with carrier frequencies of 1.41 and 1.5 MHz with the square wave amplitude modulation at 500 Hz. The modulated OMF, having twice less mean power than that without modulation, nevertheless caused disorientation at a lower amplitude of the carrier wave than the unmodulated OMF. Since the prediction of the photochemical theory is exactly the opposite, we conclude that the effect of OMFs on the magnetic orientation of birds is not related to the decoherence of electron spins in cryptochrome. Instead, we suggest that OMFs are perceived by a separate sensory system, most likely based on electromagnetic induction. This hypothetic sensory system, specialized at the detection of magnetic perturbations caused by solar flares or thunderstorms, might react to shape rather than to the mean power of the detected signal, which would explain its higher sensitivity to modulated OMFs.
Comparative analyses of diversity in human populations often rely on historical, archival and observational data, which are systematically shaped by uneven documentation, preservation and research attention. Such collection bias can distort comparisons across populations, regions or time periods, obscuring genuine patterns of variation or generating spurious ones. Standard approaches that equalize sample size or sampling effort implicitly assume comparable sampling completeness, which is an assumption rarely satisfied in human and historical datasets. Here, we evaluate coverage-based standardization, adapted from ecological diversity estimation, as a general framework for comparative diversity analysis under biased and incomplete observation. Using population-level simulations, we show that coverage-based approaches reliably recover true diversity relationships across multiple, qualitatively distinct mechanisms of collection bias, whereas sample-size-based methods yield systematically distorted inferences. We illustrate the utility of this framework with an application to a large historical cultural dataset, where correcting for uneven documentation substantially refines inferred diversity patterns. By conditioning comparisons on sampling completeness rather than raw sample size, coverage-based standardization offers a principled and broadly applicable solution for comparative research in any domain where observation is biased, incomplete or historically contingent.
