International Journal for Numerical Methods in Biomedical Engineering

Classical neuronal cable theory relies on quasi-static electric field approximations and neglects magnetic induction, Lorentz force coupling, and transient electromagnetic currents, limiting its ability to fully characterize action potential propagation within geometrically branched axons and dendrites. This work develops a coupled Maxwell-electromagnetic cable framework by integrating finite-difference time-domain (FDTD) solutions of Maxwells equations with extended Hodgkin-Huxley and Fitzhugh-Nagumo membrane dynamics, incorporating magnetic gating perturbations, electromagnetic trans-membrane currents IEM, and nanoscale quantum corrections for thin neural segments. Controlled propagation experiments are designed to quantify deviations from standard cable predictions across asymmetric and symmetric axonal bifurcation geometries. Numerical results demonstrate that inductive magnetic effects lower the critical branch radius for junction conduction failure and break symmetric action potential invasion in geometrically identical child branches under external transverse magnetic fields. An electromagnetic corrected geometric ratio GREM is proposed to revise impedance-matching conditions at branch points, accounting for size-dependent axial current imbalance induced by magnetic and displacement currents. Parent axon conduction velocity deviates substantially from the canonical [Formula] scaling law when electromagnetic feedback and quantum charge distributions are included, triggering early signal blockage at large cable diameters. Collectively, this study establishes that quasi-static cable models underestimate electromagnetic corrections to propagation speed, waveform shape, and bifurcation transmission fidelity; the coupled Maxwell-cable framework provides a comprehensive multi-physics tool for modeling electrodynamic signal behavior in complex neuronal architectures.

Cerebrovascular pulsatility drives measurable brain tissue deformation and has been associated with ageing and a range of neurological disorders. Yet how pulsatile haemodynamic forces are transmitted through deformable cerebral arteries into the surrounding brain remains poorly understood, particularly in anatomically realistic vascular geometries. Existing computational approaches have largely treated cerebral fluid and tissue mechanics separately or relied on idealised geometries, limiting our ability to determine how vascular anatomy simultaneously governs intraluminal haemodynamics and extravascular mechanical loading. Here, we develop an image-derived three-dimensional computational framework that jointly resolves pulsatile blood flow, arterial wall deformation and surrounding brain tissue motion in representative cerebral arteries. Four arterial segments, including the middle cerebral artery, middle cerebral artery bifurcation, basilar artery and internal carotid artery, are reconstructed from high-field (5 Tesla) magnetic resonance imaging data of a healthy subject. A finite-deformation fluid-structure interaction model is established by coupling non-Newtonian blood flow, hyperelastic arterial wall and hyper-viscoelastic brain tissue. The predicted tissue response is benchmarked against in vivo magnetic resonance elastography measurements of cardiac-induced volumetric strain over a cardiac cycle. Results reveal spatially localised arterial and tissue deformation whose magnitude and distribution are strongly governed by vascular geometry and wall thickness. Among the segments examined, the internal carotid artery exhibits the largest deformation response, while reduced wall thickness increases strain transmission into the surrounding tissue. Geometrically complex regions also exhibit greater spatial heterogeneity in near-wall haemodynamic metrics. These findings demonstrate that cerebral vascular anatomy simultaneously shapes intraluminal haemodynamics and extravascular mechanical loading. By integrating image-derived vascular anatomy, coupled blood-vessel-brain mechanics and in vivo benchmarking within a unified framework, this study provides a mechanically consistent reference for healthy cerebral pulsatility and establishes a foundation for quantifying how blood-vessel-brain interactions are altered under pathological conditions.

Pulmonary hypertension (PH) is characterized by progressive structural and mechanical remodeling of the pulmonary vasculature, yet few computational frameworks directly link disease mechanisms to longitudinal progression and therapeutic response. In this study, we utilized a multiscale pulmonary arterial growth and remodeling (G&R) framework to capture evolving functional metrics from rat models of PH. This framework couples morphometric tree hemodynamics, constrained mixture theory-based wall mechanics, and maladaptive cellular remodeling. Disease progression was driven by three mechanistically interpretable parameters governing excess smooth muscle production, remodeling activation, and passive stiffening. These parameters were calibrated to longitudinal monocrotaline (MCT) measurements of pressure, wall thickness, and stiffness from prior work using a multiobjective optimization. To show the predictive value of this model, we simulated therapeutic intervention within the same disease-specific framework by using functional cell-level responses to therapy to inform changes in parameter values. Calibration to the study-specific MCT dataset reproduced the temporal increases in pressure, wall thickness, and stiffness, demonstrating that the model could capture multiple features of vascular remodeling simultaneously, with R2 values of 0.81, 0.83, and 0.95, respectively. Simulated treatment reduced pressure, wall thickness, and stiffness. Predicted pressure and wall-thickness responses agreed closely with the corresponding experimental treatment effects, whereas stiffness recovery was overpredicted, suggesting that additional mechanisms may contribute to persistent vascular stiffening after intervention. The framework also captured the overall progression of pulmonary pressure increases across both aggregated MCT and Sugen-hypoxia datasets, suggesting utility across studies and animal models. This work outlines a physics-based, multiscale framework that simulated quantities of direct clinical interest in a mechanistically interpretable platform for linking pulmonary vascular remodeling and treatment response. It supports comparisons across experimental phenotypes and interventions while identifying where constitutive refinements are needed to improve predictive capability across phenotypes.

Cerebral aneurysms are abnormal outpouchings of arteries within the brain and occur in [~]1 in 30 adults. Their initiation, growth, and rupture have been linked to focal blood flow abnormalities--often termed "disturbed" or "hostile" hemodynamics--but commonly-used hemodynamic metrics yield conflicting associations with pathology and lack a unifying mechanistic interpretation. Building on a theoretically-grounded link between wall shear stress and near-wall vorticity, we hypothesized that a topology-based description of near-wall flow can operationalize the concept of hostile hemodynamics in a reproducible way. Inspired by atmospheric tornadic phenomena, we sought a principled taxonomy of coherent near-wall fluid structures with potential mechanobiological and clinical implications. Using high-fidelity computational fluid dynamics simulations in anatomically realistic geometries, we identified coherent near-wall fluid structures whose organization mirrors well-studied atmospheric phenomena: tornado-like columnar rotating cores; downburst-like nonrotating wall-impinging jets with tangential outflow, roll-cloud-like tangential vortices; and mixed configurations. These tornadic events on the aneurysm luminal surface were identified from wall shear stress topology, consistent with its theoretical connection to near-wall vorticity kinematics. The presence of tornadic phenomena--and their imprints on the aneurysm wall--was independently observed in vivo using 4D flow magnetic resonance imaging. By translating concepts from atmospheric physics into vascular biomechanics, this topology-based framework yields a unified mechanistic language for describing near-wall hemodynamics, resolving blood flow complexity into interpretable and reproducible coherent fluid structures, enabling standardized hemodynamic phenotyping, and supporting hypothesis-driven studies of aneurysms and other cardiovascular diseases where greater fluid-mechanical specificity and interpretability may strengthen links between mechanobiology and clinical risk.

Muscle architecture shapes muscle function and changes with age, growth, training and disease, yet quantifying three-dimensional (3D) muscle architecture in vivo remains challenging. We introduce a hybrid fascicle tractography approach for freehand 3D ultrasound data that accurately reconstructs 3D muscle fascicles with respect to an objective, anatomically relevant coordinate system defined by the muscle's central aponeurosis. The hybrid approach combines Hessian-based fascicle detection with wavelet-based refinement to generate volumetric fascicle orientations. In a synthetic dataset with known ground truth, fascicle orientations and lengths were estimated with errors of [≤]2{degrees} and ~1.5%, respectively. In vivo, the approach detected physiologically plausible fascicle lengthening in the human tibialis anterior following a passive plantar flexion rotation, whereas diffusion tensor imaging of the same muscle did not. The proposed method enables anatomically relevant, objective and non-invasive quantification of 3D muscle architecture in vivo, providing a practical framework for applications in clinical and applied muscle physiology.

Maternal thermal strain is associated with adverse pregnancy outcomes, yet fetal temperatures cannot currently be directly measured, limiting quantification of fetal thermal strain. Here, we develop two steady-state models for estimating internal temperatures in a near-term fetus. First, we improve the only previously published human fetal thermoregulation model, deriving a closed-form solution within its simplified uniform-cylinder representation. Second, we introduce a multilayer, anatomically segmented model that resolves tissue-specific temperatures. Both couple the fetal body to central blood pool and amniotic fluid compartments and incorporate a new placenta-umbilical cord heat-exchanger representation. Predictions agree with available intrauterine scalp measurements, with fetal core and head-center temperatures approximately 0.5{degrees}C and 0.8{degrees}C above maternal core, respectively. Physiologically plausible changes in umbilical cord heat-exchanger effectiveness or blood flow increased fetal temperatures by approximately 0.3{degrees}C. These models enable estimation of otherwise inaccessible temperatures, while the multilayer formulation lays a foundation for transient, coupled maternal-fetal thermoregulation modeling.

BackgroundPlatelet activation in flowing blood is a multiscale process in which vessel-scale hemodynamics, red blood cell (RBC) mechanics, adhesive receptor interactions, and intracellular signalling jointly determine thrombotic risk. Individual components are well studied, but a single reduced description that carries each explicitly from vessel-scale flow to mechanosensitive calcium entry, with dimensionally consistent couplings, remains uncommon. ObjectivesWe develop and analyse a reduced, six-module mechanobiological framework for platelet priming spanning the cascade from hemodynamic shear to mechanosensitive calcium entry, and we delineate which elements are supported by existing evidence and which are new, testable hypotheses. MethodsThe framework comprises six coupled modules: (I) hemodynamic forcing from the incompressible Navier-Stokes equations, with an objective principal-strain-rate measure for extensional flow; (II) RBC-mediated platelet margination and near-wall delivery, closed by a near-wall arrival flux; (III) von Willebrand factor (VWF) activation with a bounded kernel and glycoprotein Ib (GPIb) catch-slip capture, resolved through an explicit contact area and a bond-dependent mobility that progressively immobilises wall-interacting platelets; (IV) a single-load membrane-stimulus formulation; (V) mechanosensitive gating and a dimensionally consistent cytosol-store calcium model with extracellular influx; and (VI) a phenomenological mechanical-memory state. We formally derive that the single-platelet stochastic dynamics and the continuum population balance form a Fokker-Planck pair, with the spatially varying diffusivity handled by an explicit drift correction. ResultsThe framework yields a family of mechanochemical dimensionless groups delineating priming regimes. Its central prediction is reformulated as a falsifiable, history-sensitive signature: in a conditioning-test protocol, a low-tension conditioning block charges the memory state, and a fixed sub-threshold test pulse then reports a delay-dependent calcium facilitation that decays on the memory time{tau} m and is distinguishable from no-memory gating, channel adaptation, and residual-calcium priming. We show explicitly that the previously proposed pulsatile-versus-monotone contrast is a nonlinear convexity/thresholding effect of the gating nonlinearity--its difference-in-differences is approximately zero-- and is therefore not a valid test of memory; the conditioning-test signature is. A second prediction links RBC stiffening to reduced near-wall delivery and captured-platelet calcium response, upstream of intrinsic platelet signalling. ConclusionsThe framework provides a dimensionally consistent, mechanistically grounded and hypothesis-generating description linking hemodynamic forcing to mechanosensitive calcium entry. It demonstrates how history-dependent platelet priming may arise from a phenomenological sensitisation state and proposes a conditioning-test protocol for comparison against adhesive, channel and intracellular-store persistence. The framework is calibratable rather than validated, and the quantitative outputs shown use representative uncalibrated parameters.

This study aimed to (1) develop an elasticity framework for the sprint force-velocity (F-V) relationship and (2) examine how maximal force (F_{0}), maximal velocity (v_{0}), and sprint distance modulate the four derived elasticity metrics, and (3) explore these elasticity metrics' interrelation. After modelling the F-V relationship differential equation, four elasticity metrics were defined as force elasticity (F_{e}), the elasticity of sprint time to F_{0}; velocity elasticity (v_{e}), the elasticity of sprint time to v_{0}; the force-velocity elasticity norm {(\mathrm{F}-\mathrm{V}}_{\mathrm{EN}}=\sqrt{F_{e}^{2}+v_{e}^{2}}), capturing the combined sprint time sensitivity to proportional changes in F_{0} and v_{0}; and the force-velocity elasticity ratio {(\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=F_{e}{\div v}_{e}), indicating which variable dominates the sprint time response. Model simulations showed that F_{e} decreased with rising F_{0} and increased with rising v_{0}, while v_{e} showed the opposite pattern. With increasing sprint distance, F_{e} decreased and v_{e} increased. Given its negligible effect on sprint time, ignoring air resistance yields a conservation law (2F_{e}+v_{e}\equiv 1), indicating that a gain in one elasticity metric necessarily diminishes the other in a fixed proportion. This framework also identifies a valley distance (d_{valley}) at {\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=2, where {\mathrm{F}-\mathrm{V}}_{\mathrm{EN}} is minimized (\sqrt{0.2}) and sprint time is least responsive to changes in F-V relationship variables. Empirical data confirmed that the two theoretical laws still hold approximately when air resistance is considered. By linking changes in F_{0} and v_{0} to sprint time across different distances, the elasticity framework provides a quantitative basis for estimating the theoretical sprint time response to documented changes in F-V relationship variables.

Continuous wave diffuse correlation spectroscopy (cw-DCS) is a noninvasive optical technique to monitor the tissues blood flow changes. This technique measures the tissue blood flow index (BFI) by evaluating the decay rate of the autocorrelation function. The derived BFI is proportional to mean squared displacements of the red blood cells considered as the fast-dynamic scatterer component of tissue in time. However, biological tissue contains static scatterer component and slow-dynamic scatterer component which affect the decay rate of autocorrelation function and as a result the derived BFI. In this study, we assessed the fractional contribution of static, slow-dynamic and fast-dynamic scatterer components of a medium in the flow index derived by cw-DCS. The measurements performed on Agar-based phantom with tube showed that presence of static scatterer component and slow-dynamic scatterer component led to substantial underestimation ({approx} 123%) of the flow index derived by Siegert relation, compared to effective diffusion coefficient of fast-dynamic scatterers components derived by modified Siegert relation and bi-exponential model. The less underestimation was observed for the corresponding parameters obtained from the liquid phantom measurements ({approx} 25%) as well as during the forearm occlusion test and respiratory challenges ({approx} 16% - 26%).

Purpose: Fibrotic remodeling of the uterus, associated with aging, disease, and environmental exposures, alters collagen organization and tissue stiffness, yet how these changes influence organ-level mechanical behavior remains poorly understood. Glutaraldehyde (GA)-induced collagen crosslinking was used as a controlled surrogate for fibrotic remodeling to determine whether image-informed inverse finite element analysis (iFEA), combined with inflation testing and micro-computed tomography (microCT), could detect and quantify the resulting changes in uterine constitutive behavior. Methods: Murine uteri (n = 6 untreated, n = 6 GA-crosslinked) underwent volume-controlled balloon inflation with simultaneous microCT imaging to quantify deformation of the inner and outer wall boundaries for iFEA. Specimen-specific Gasser-Ogden-Holzapfel (GOH) finite element models were optimized by adjusting model parameters to reproduce experimentally measured wall contours throughout inflation. Model performance was evaluated using contour root mean square error (RMSE), and parameter identifiability was assessed through sensitivity analyses. Results: GA treatment significantly increased inflation work, linear stiffness, and maximum inflation resistance (p < 0.001). The iFEA framework accurately reproduced experimental deformation (RMSE < 3%) and revealed significant increases in the estimated GOH parameters C10 (9.2-fold), k1 (2.0-fold), and k2 (2.7-fold), consistent with increased effective tissue stiffness and a shift toward earlier collagen fiber recruitment. Sensitivity analyses demonstrated unique, well-defined minima for all parameter combinations. Conclusion: Image-informed iFEA provides a quantitative framework for relating collagen remodeling to organ-level uterine mechanics through specimen-specific constitutive parameter estimation. This approach establishes a foundation for investigating the mechanical consequences of uterine fibrosis and other remodeling processes.

Tumor spheroid mechanics arise from both the mechanical properties of individual cells and the adhesive interactions that organize them into tissues. The relative contribution of these two factors to the bulk mechanical behavior, however, remains difficult to disentangle experimentally. Here, we develop a computational model of micropipette aspiration to compare the mechanical response of isolated cells and multicellular spheroids within a common computational framework. By independently varying single-cell stiffness and cell-cell adhesion, we quantify their effects on aspiration dynamics, effective elastic modulus, and viscoelastic relaxation. Our results show that increasing single-cell stiffness substantially alters the mechanics of isolated cells but has limited influence on the effective elastic modulus of multicellular spheroids. In contrast, changes in cell-cell adhesion produce pronounced effects on spheroid effective elastic modulus. Nevertheless, both parameters increase the retardation time governing the transition from the initial elastic response to long-time viscous deformation. These findings suggest that multicellular elasticity is governed primarily by intercellular mechanical coupling, whereas the dynamical response to applied stress depends jointly on cell-scale mechanics and cell-cell adhesion.

The study of tissue dynamics has been stimulated during the last decades thanks to the use of quantitative descriptions, with the development of several theoretical and computational frameworks, many of them revolving around the notion of reaction-diffusion systems, eventually with additional structure variables beyond time and space. The use of structure variables can accommodate phenotypic traits. In this work, we study a family of competition models, where a given population depends on a resource (e.g. oxygen) and several populations are competing for it. Our quantitative description incorporates phenotypic traits and heterogeneity at the level of cell cycle variations, which influence replication rates via oxygen consumption. This enables us to replicate the fitness of specific subpopulations to environmental conditions (e.g. oxygen shortage or external influences). Using numerical simulations, we show that such models display dynamical pattern formation in the form of coupled travelling wave profiles that expand or retreat at the same wave speed. The full theoretical analysis of such dynamics is quite involved; to circumvent this difficulty, we introduce a quasi-stationary approximation for the resource dynamics. We find that this approximation can reproduce the overall behaviour very accurately, with the additional benefit of allowing theoretical treatment of the reduced model. In this way, we provide estimates on the wave speed which are numerically shown to be robust across a wide range of macroscopic parameters of the full model. The wave speeds are thus found to depend strongly on the proliferation rate of the fittest population, resembling a winner-takes-all dynamics.

Deep brain stimulation (DBS) modeling relies heavily on biophysical neuron models to estimate neural activation thresholds and predict stimulation spread. In this study, we systematically compared a widely adopted axon model, the McIntyre-Richardson-Grill (MRG) model (Model I), with a more detailed biophysical model, the Cohen model (Model II), to assess how structural and electrophysiological differences affect predicted DBS outcomes. Electric field distributions generated by 2202 DBS lead were applied to the neuron models as extracellular input stimuli. Both models were simulated under biphasic pulse stimulation across varying axon-electrode distances, pulse widths, and stimulation frequencies. Activation distances ranged from approximately 2 to 10 mm depending on stimulation parameters and contact location. At 2 mA, Model I achieved an activation distance of 6 mm, whereas Model II reached 10 mm, indicating greater excitability. Across matched fiber tracts, threshold differences ranged from -1.40 mA to 0.27 mA, with Model II requiring lower thresholds in 97.7% of cases. Both models showed a strong inverse relationship between pulse width and activation threshold. However, frequency responses differed: Model II exhibited increasing thresholds at higher frequencies, while Model I showed a slight decrease. Machine learning regressors trained on distance, pulse width, and frequency achieved high predictive accuracy, with Gradient Boosting performing best. Model II demonstrated superior prediction metrics (R^2 = 0.986; RMSE = 0.045 mA; MAE = 0.034 mA) compared to Model I (R^2 = 0.977; RMSE = 0.089 mA; MAE = 0.068 mA). Overall, both models reliably estimate DBS-induced activation, but structural differences significantly affect excitability and frequency-dependent behavior. With appropriate awareness of their respective strengths and limitations, either model can be used to derive activation distances for estimating electric field isolevels and the volume of tissue activated in patient-specific DBS simulations.

BackgroundCardiovascular disease risk is higher in men than in women. Although sex differences in aortic wall adaptation following antihypertensive treatment have been reported in acute hypertension models, the response after gradually developing hypertension, which mimics human essential hypertension, remains unclear. This study investigated sex differences in aortic wall adaptation following acute blood pressure reduction after gradually developing hypertension. MethodSeventeen-week-old spontaneously hypertensive rats (SHRs) were assigned to the Hypertensive group or the antihypertensive (Reversal) group (N = 5/sex each). The Reversal group received the antihypertensive drug captopril for 4 weeks to maintain systolic blood pressure below 130 mmHg. Age-matched Wistar Kyoto rats (N = 3/sex) served as normotensive (Normal) group. After the experimental period, arterial wall thickness, circumferential wall stress, smooth muscle cell phenotype, and histological changes were evaluated. ResultsAntihypertensive treatment significantly reduced systolic blood pressure in both sexes. Both male and female SHRs exhibited elevated circumferential wall stress during the gradual development of hypertension. In females, antihypertensive treatment significantly reduced medial thickness compared with the Hypertensive group, whereas males showed no reduction. Circumferential wall stress in female Reversal group did not differ significantly from either the Hypertensive or Normal group, whereas males exhibited a significant reduction in circumferential wall stress compared with the Hypertensive group. Furthermore, the reduced collagen area fraction in the Hypertensive group returned to the normotensive levels only in females following antihypertensive treatment. ConclusionThese findings indicate that vascular remodeling induced by gradually developing hypertension is more effectively reversed by antihypertensive treatment in females than in males.

Adolescent idiopathic scoliosis (AIS) alters spinal geometry and may influence the biomechanical response of the spine during functional postures. However, posture-dependent changes in spinal loading and paraspinal muscle forces in AIS remain poorly understood. This study investigated the effects of trunk posture on intervertebral loading and paraspinal muscle forces using a subject-specific musculoskeletal model of an adolescent with AIS. The spinal deformity was reconstructed from biplanar radiographs and incorporated into a full-body musculoskeletal model. Flexion, extension, lateral bending, and axial rotation were simulated at three incremental magnitudes, with motion distributed across the thoracolumbar spine. Intervertebral compressive and lateral forces around the curve apex and forces in the erector spinae (ES) and multifidus (MF) muscles were evaluated. Trunk flexion produced the greatest compressive loading, reaching 337 N at the curve apex and 372 N two levels below the apex at 30{degrees} flexion. Lateral bending produced pronounced direction-dependent loading: concave-side bending increased lateral forces, whereas convex-side bending increased compressive forces. Axial rotation produced similar but smaller direction-dependent changes. Paraspinal muscle forces were consistently asymmetric, with concave-side dominance of the ES and convex-side dominance of the MF. Flexion and convex-sided movements generally produced greater muscle imbalance, while increasing posture magnitude amplified spinal loading and muscle forces. These findings demonstrate that trunk posture, movement direction, and magnitude substantially influence the biomechanical environment of the scoliotic spine and should be considered when evaluating spinal mechanics in AIS.

A classical study found no excitation transfer when isolated cardiomyocytes were placed side by side, whereas a recent paper reported action potential transfer in carrdiomyocytes placed end to end. We use nanoscale numerical simulations based on the full Poisson-Nernst-Planck equations to investigate whether these apparently opposing observations can be explained by the different geometrical configurations. The computations show that in the end-to-end configuration, ephaptic coupling occurs when the intercellular cleft is sufficiently narrow and a sufficiently large fraction of the sodium channels is localized at the intercalated disc. Coupling is strengthened when the sodium channels are concentrated in fewer clusters and when ionic diffusion within the cleft is reduced. Under these conditions, excitation transfer occurs on a timescale consistent with rapid cell-to-cell activation. Conduction depends biphasically on cleft width and terminates abruptly beyond a critical width. Localization of potassium channels at the intercalated disc has only a moderate effect, whereas gap junctions substantially improve conduction and reduce the relative contribution of ephaptic coupling. In the side-by-side configuration, excitation transfer does not occur under physiological conditions and requires highly flattened cells, minimal separation, and unrealistically strong sodium-channel clustering. The different outcomes of the side-by-side and end-to-end experiments can therefore be explained by the fundamentally different geometrical conditions for ephaptic coupling.

Microbial fuel cells (MFCs) have been widely investigated as decentralized bioelectrochemical systems capable of converting organic substrates into electricity. However, their long-term autonomous operation is constrained by substrate depletion in the anode compartment, leading to metabolic starvation of electroactive biofilms and a decline in power output. Conventional MFC design treats substrate crossover through the membrane separator as a parasitic loss that reduces coulombic efficiency. In this work, we propose a conceptual inversion of this paradigm by considering controlled cathodic-to-anodic substrate crossover as a passive mechanism to sustain basal microbial metabolism during periods of substrate scarcity. A transport-reaction framework is developed to quantify the balance between membrane-mediated substrate flux and microbial maintenance demand within the anode biofilm. Based on this balance, a dimensionless maintenance crossover Damkohler number (Dam) is introduced to define three operational regimes: starvation-dominated (Dam >> 1), balanced autonomous (Dam {approx} 1), and crossover-dominated (Dam << 1). The framework integrates membrane transport theory with biofilm kinetics to evaluate the effects of separator properties, substrate gradients, and current-dependent electro-osmotic transport on system stability. Order-of-magnitude analysis indicates that achievable crossover fluxes span several orders of magnitude depending on separator characteristics, suggesting that membrane properties critically influence system behavior. This perspective reframes substrate crossover from a loss mechanism to a potential design variable, offering a conceptual tool for enhancing resilience and guiding separator selection in MFCs intended for long-duration, and low-maintenance operation. HighlightsO_LIControlled crossover can sustain microbial metabolism in MFCs C_LIO_LIIntroduces maintenance crossover Damkohler number (Dam) C_LIO_LIIdentifies regimes for autonomous and starvation operation C_LIO_LILinks membrane properties to long-term system stability C_LIO_LIReframes crossover as a design variable, not only a loss C_LI

The development of neural connections in the brain results from a complex interplay between biological processes and mechanical forces. A key question in neuroscience is how physical forces and the mechanical properties of brain tissue influence the formation of structural connections. Here, we demonstrate that mechanical forces play an essential role in shaping the emergence of short-range connections, particularly U-shaped fibers that link neighboring regions of the cortex. Using a computational model that incorporates our "stress-dependent axon reorientation" hypothesis, we simulate how growing axons respond to the mechanical stress field generated by cortical folding. Our results suggest that axonal growth and reorientation may be strongly influenced by local mechanical cues, helping establish the organization of these short-range pathways. Supported by in vivo diffusion tensor imaging and histological observations, our findings provide a physical explanation for why these fibers predominantly adopt U-shaped trajectories, and why connections between gyri (ridges) are more prevalent than those between sulci (valleys) or spanning gyri and sulci. These results suggest that understanding the mechanics of brain folding is critical for fully explaining the formation of brain connectivity and its variations in health and disorder. Teaser: Mechanical forces during cortical folding guide the formation of short association fibers in the brain.

Within the studies of stochastic gene expression, apart from the variability of copy number of gene products, the problems of threshold crossing of those products are biologically important as they often lead to terminal cellular events. Here, we study the threshold crossing problem of the messenger ribonucleic acid (mRNA) and present an exact probability distribution of first passage times in Laplace space. The function furnishes moments of any order and also predicts the characteristic time of the exponential tail of the distribution, which we match against Gillespie simulations. We find that all the measures of relative fluctuations of the threshold crossing times show U-shapes within this simple model of mRNA, as was found earlier in more mathematically involved models of threshold crossing time statistics of proteins. Furthermore, we extend the exact formula to include the phenomenon of DNA duplication and the corresponding doubling of transcription rate. As expected, the distribution varies considerably depending on the onset of the duplication stage within the cell cycle.

Diffusion within the cytoplasm is fundamental to numerous biological processes. Fluorescence recovery after photobleaching (FRAP) is one of the most common method for quantifying molecular diffusivity in living cells using standard laser scanning confocal microscopy (LSCM). However, accurately measuring fast cytoplasmic diffusion (typically >10 m^2/s) is challenging due to rapid recovery kinetics, weak signal-to-noise ratios, post-bleach signal artifacts, and spatial restrictions affecting normalization. While individual challenges have been addressed in specific contexts, a simple and robust framework to quantify cytoplasmic diffusivity remains elusive. Here, we present a FRAP methodology specifically designed to overcome these obstacles. By utilizing the Gaussian function -- the impulse response (ImpRes) of the diffusion equation in an infinite medium -- our approach leverages the full spatiotemporal dataset through a single-equation three-parameter fitting procedure, thus releasing restrictions to small regions of interest and arbitrary initial time-points. The methodology was validated on three datasets of increasing complexity: in silico simulated recovery profiles, in vitro data from FITC-dextran in glycerol solution, and live-cell imaging of free cytoplasmic GFP. Systematic comparison with existing models demonstrates that the ImpRes approach significantly reduces sensitivity to noise and imperfect fluorescence normalization, while remaining robust against short-term biases, such as transient probe photo-activation. Given its robustness under realistic experimental conditions and its ease of implementation, the proposed FRAP methodology provides a reliable tool for quantitative cytoplasmic analysis.