Deformation-induced injury of white matter nerve bundles is prevalent in central nervous system injuries, leading to a series of neurological dysfunctions. To describe the effect of deformation injury on the physiological electrical signals of neurons, several axonal electrophysiological damage models based on the Hodgkin-Huxley equations have been proposed. However, none of these models can fully characterize the results of white matter nerve stretching experiments under different rates and amplitudes. Building on previous work by peers, we propose a novel axonal deformation damage model that comprehensively considers the coupled effects of sodium and potassium channel damage on gating variable rate constants, as well as the differences in mechanical properties and injury among individual axons during deformation—with these differences continuously modeled by a statistical distribution. The proposed model can not only reasonably describe the sodium left-shift phenomenon (a key post-injury feature) but also accurately reproduce the neuro-electrophysiological responses of nerve fascicles under various stretching conditions. This new neuro-electrophysiological model for nerve fascicle injury exhibits good universality and provides a mechanical mechanism-based reference for future clinical treatment strategies of nerve injury.
Calibration of mechanistic cardiovascular models is a central barrier to their use in population analysis and patient-specific simulation, particularly in settings where key physiological variables are unobservable and multiple parameter combinations can reproduce the same haemodynamic targets. In this work, we present Embedded Feedback Controller (EFC), a calibration framework for ODE-based lumped-parameter cardiovascular models in which selected physiological parameters are promoted to dynamic states and driven toward prescribed targets through embedded controller equations. By exploiting the qualitative structure of the governing equations, EFC enforces physiologically consistent parameter-variable relationships and converges to the single solution admitted by an over-determined set of targets, reproducibly and independently of the initial conditions, at a cost that scales efficiently with model complexity. The framework is demonstrated in silico, using a mechanistic cardiovascular model to generate virtual paediatric populations spanning normal physiology and two septic shock phenotypes (warm and cold shock) from literature-derived target ranges, achieving <1% residual error across pressures, flows, and compartmental volumes. The resulting parameter distributions are consistent, by construction with theoretical haemodynamic adaptations in paediatric sepsis, including alterations in vascular resistance, compliance, cardiac elastance, and effective blood volume. Importantly, persistent calibration residuals arise when target combinations are structurally incompatible with the model and the parameter carrying the residual is held at a bound, providing an explicit and interpretable diagnostic of feasibility limits. These results establish EFC as a general, scalable calibration strategy for mechanistic cardiovascular models and a practical foundation for virtual population generation and future patient-specific digital twin applications in critical care.
In grant applications, oral presentations can be an integral part of the evaluation process. Funding pitches are a specific form of pitch presentations delivered during competitive review procedures to complement a written proposal. Their goal is to convince reviewers of both your project's strength and excellence as well as your suitability as a researcher. This paper offers ten simple rules on how to prepare and deliver effective funding pitches, with a particular focus on content selection, clear structuring, and convincing scientific argumentation. We also highlight the importance of practice and feedback and share practical strategies on how to handle questions and critical discussion professionally.
Zero inflation remains a major challenge in microbiome differential abundance analysis, often resulting in inflated type I error rates or reduced statistical power. Although numerous methods have been proposed to address excess zeros, many existing approaches do not explicitly distinguish between distinct zero-generating mechanisms, and censoring-based modeling perspectives remain relatively underexplored in microbiome data analysis. To advance methodological development in this area, we introduce a novel censoring-based modeling perspective for microbiome differential abundance analysis. Specifically, we develop a joint mixture Tobit (joint mTobit) method for zero-inflated microbiome data. Building on a mixture Tobit formulation, the model incorporates a point-mass component to represent structural zeros, while modeling latent microbial abundance via a Tobit regression component. The proposed framework jointly links disease status, latent true microbial abundance, and relevant covariates within a unified probabilistic model, allowing appropriate adjustment for confounding factors and facilitating more reliable statistical inference. By explicitly modeling the latent abundance underlying observed counts, the joint mTobit method improves estimation stability and enhances detection power under zero inflation. Extensive simulation studies demonstrate that the joint mTobit method achieves effective type I error control while maintaining high statistical power and stable coefficient estimation across a wide range of settings. Application to a real-world colorectal cancer and adenoma microbiome dataset further illustrates its ability to identify biologically meaningful differentially abundant taxa. Overall, this work develops a joint mTobit modeling framework for zero-inflated microbiome data, enabling inference on latent microbial abundance and providing a censoring-based statistical framework for investigating disease–microbiome associations in differential abundance analysis.
Introduction Human mobility across the Brazilian Amazon—shaped by frontier development, land-use change, mining activity, and climatic shocks—remains a major barrier to malaria elimination. Brazil aims to eliminate Plasmodium falciparum (Pf) by 2030 and Plasmodium vivax (Pv) by 2035, yet malaria incidence has plateaued since 2020. We quantify parasite-specific mobility and assess how importations drive malaria reintroduction and reestablishment in areas approaching elimination. Methods We analyzed 5,521,411 de-identified malaria cases from Sivep-Malaria (2003–2023) across 808 municipalities in the nine-state Brazilian Amazon. We (i) used multivariable monthly sequence analysis to classify “Municipal Malaria Importation Trajectories” (MMITs) using local/imported composition, incidence per 1,000, and source/sink network typologies; (ii) characterized mobility networks by parasite species and locality type (rural, urban, settlement, indigenous, mining); and (iii) developed a parasite-specific rule-based classification procedure to identify reintroduction, and defined reestablishment as sustained local transmission. Results Sequence analysis produced five MMIT clusters, spanning municipalities nearing elimination to areas with sustained high local transmission and distinct profiles of declining transmission with intense vs. moderate importation. Overall, 959,281 cases (17.38%) were imported. Cases were predominantly Pv (81.68%), with Pf comprising 17.15%; Pf was more common among imported than locally acquired infections. Mobility patterns shifted after mid-2020: by 2023, > 50% of exported cases originated in mining localities despite ~0.2% land coverage, with Pf importation increasingly concentrated in municipalities overlapping the Yanomami Indigenous Land. From 2006–2023, we identified 63 Pf and 221 Pv reintroduced locally acquired cases following ≥3 years of zero local transmission, leading to reestablishment in 6 and 23 municipalities, respectively, and generating 864 subsequent Pf and 6,524 subsequent Pv cases. Post-reintroduction transmission persisted for long periods (mean 762 days for Pf ; 815 days for Pv ). Conclusion Malaria elimination in Brazil is increasingly hindered by parasite-specific mobility, especially linked to mining–indigenous confluences, and by repeated reintroduction in receptive municipalities nearing elimination (notably along the arc of deforestation). Mobility-informed surveillance and targeted reactive strategies are essential to achieve and sustain Brazil’s malaria elimination goals.
