Search Results (363)

Continuing our work in the field of synthesis and research of amino acids, their derivatives, and cyclization products, in this work, we synthesized various 3-(6-bromo-2-oxo-1,3-benzoxazol-3(2H)-yl)propanoic acid derivatives and investigated their antimicrobial activity. A total of eighteen synthesized chemical compounds (No. 1–18), including several structural analogues (e.g., 3a, 3b, 4a–4e, 8a–8m, 9a–9d), were evaluated for their antibacterial properties. The antibacterial activity was assessed using the Kirby–Bauer disk diffusion method, and inhibition zone diameters (mm) were measured against five representative bacterial strains: S. aureus, MRSA, B. subtilis, E. coli, and P. aeruginosa. The minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of the most active synthesized compounds were determined against representative Gram-positive and Gram-negative bacterial strains, including S. aureus, MRSA, B. subtilis, and E. coli. Overall, these results indicate that the tested compounds display selective antibacterial activity, mainly against Gram-positive bacteria, with compound 12 emerging as the most promising derivative in the series. The antibacterial activities of several synthesized compounds were systematically evaluated against S. aureus and MRSA over a 24 h incubation period, with optical density measured at ten time points. Bacterial growth was monitored spectrophotometrically at 600 nm (OD₆₀₀) at 1, 2, 3, 4, 5, 6, 7, 8, 20, and 24 h, enabling a detailed assessment of growth kinetics and the temporal dynamics of inhibition. The effect of compound 11 on the growth kinetics of S. aureus was evaluated by quantifying viable bacterial counts (log₁₀ CFU/mL) over a 6 h incubation period, and the results are presented in the time–kill curve. Compound 11 was selected for this experiment because it exhibited the most pronounced antibacterial activity against S. aureus in the disk diffusion assay. The cytotoxicity of compounds 9a, 11, 12, and 13 was evaluated at concentrations ranging from 125 to 1.95 µg/mL. The results showed a clear, concentration-dependent decrease in cytotoxicity for all tested compounds. The molecular structure of compound 3a was confirmed by a single-crystal X-ray diffraction. Full article

Origin–Destination matrices (ODms) are a fundamental input for public transport planning and optimization, as they characterize travel demand across a network. Traditionally estimated from user surveys, ODms are now increasingly inferred from large-scale automatically collected data, such as Automated Fare Collection (AFC), Automated Passenger Counting (APC), and Automated Vehicle Location data (AVL). This review focuses on the reconstruction of static ODms in public transport systems, while accounting for studies that exploit dynamic or short-term observations when these are used to infer static or quasi-static demand patterns. We provide a transversal synthesis of OD estimation approaches by jointly analyzing data sources, modeling assumptions, uncertainty handling, and validation strategies. A structured comparative table summarizes representative case studies across different data contexts, objectives, and methodological families. Beyond a descriptive overview, this review identifies key research gaps, including the lack of uncertainty-aware benchmarking frameworks, the limited propagation of uncertainty across modeling stages, and the strong dependence of reported performance on data quality and validation references. These findings highlight that OD estimation performance is context-dependent and that methodological choices should be aligned with data availability, modeling objectives, and acceptable assumptions rather than with reported accuracy alone. Full article

Lignin pyrolysis is a pivotal route for biomass valorization, yet the intricate radical reaction network involved results in ambiguous hydrogen/oxygen transfer pathways and product formation mechanisms, severely impeding precise control over directed conversion processes. This study employed a combination of multi-isotope tracing techniques and GC-MS analysis to elucidate the formation mechanisms of four phenolic products during the 500 °C hydrothermal pyrolysis of dealkaline lignin. Experiments using D₂O and H₂¹⁸O revealed that the M + 2 signal was predominantly derived from double deuterium substitution, with an abundance difference spanning 13–81 folds. Phenol exhibited the highest M + 1 abundance (3.947) due to the full exposure of its exchangeable hydrogen sites, while its M + 2 abundance ranked second only to that of 2-methylphenol. For 2-methylphenol, the hyperconjugation effect of the ortho-methyl group activated the phenolic structure, leading to the highest M + 2 abundance among all products (M + 2/M + 1 = 2.3). In contrast, 3-methylphenol showed relatively low abundances (M + 2/M + 1 = 1.67) because the meta-methyl group lacked activating effects and introduced steric hindrance. For guaiacol, the steric hindrance of the methoxy group completely overshadowed its electronic activation effect, resulting in the lowest M + 2 abundance (1.545). CD₃OD tracing experiments and the absence of detectable M + 3 peaks confirmed that the methyl groups in 2-methylphenol and 3-methylphenol were entirely endogenous to the structural units of lignin itself. By precisely tracking the migration pathways of hydrogen and oxygen, this study revealed that hydrogen transfer dominated the pyrolysis process, while oxygen transfer was hindered and methyl groups exhibited endogenous characteristics. These findings establish a mechanistic foundation for designing efficient catalysts tailored to lignin pyrolysis and for rationally steering product selectivity. Full article

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems. Full article

Background: Orthodontic appliances introduce new surfaces into the oral cavity that can modulate biofilm formation and potentially increase the risk of white spot lesions. Material-dependent differences in surface roughness, wettability and geometry may influence early colonization by Streptococcus mutans, a key cariogenic pathogen. Objectives: To compare early adhesion and biofilm formation of Streptococcus mutans on five commonly used orthodontic materials: stainless-steel (SS) and nickel–titanium (NiTi) archwires, metallic and ceramic brackets, polymethyl methacrylate (PMMA) acrylic resin. Materials and Methods: Standardized specimens were prepared, polished when applicable, sterilized, and conditioned in artificial saliva. The tested materials included SS and NiTi archwires (3M Unitek, Monrovia, CA, USA), metallic and ceramic brackets (Ormco, Orange, CA, USA), and PMMA acrylic resin (GC Corporation, Tokyo, Japan). Early adhesion (CFU), biofilm biomass (crystal violet), and metabolic activity (XTT) were quantified after incubation with S. mutans. Surface roughness (Ra) and contact angle were measured, and correlations with microbiological endpoints were assessed. Results: A clear material-dependent gradient was observed. Stainless steel showed the lowest early adhesion and biofilm formation (5.20 ± 0.28 log₁₀ CFU·cm⁻²; CV OD₅₉₀ = 0.60 ± 0.14), followed by NiTi, metallic brackets, and ceramic brackets, while PMMA exhibited the highest bacterial load and biofilm biomass (6.09 ± 0.32 log₁₀ CFU·cm⁻²; CV OD₅₉₀ = 1.10 ± 0.17). Overall differences between materials were statistically significant (p < 0.0001). Surface roughness and contact angle positively correlated with bacterial colonization. Conclusions: Early S. mutans colonization is strongly influenced by orthodontic material properties, with smoother and less hydrophobic surfaces showing reduced biofilm formation. PMMA and bracket structures may pose higher cariogenic risk during treatment. These findings support the development of surface-engineered or biofilm-resilient orthodontic materials. Full article

This study investigates the improvement of Ni/SiO₂-Al₂O₃ catalysts in the dry reforming of methane (DRM) process by detailed adjustments of calcination temperature (600–900 °C) and duration (1–9 h). N₂ physisorption, H₂-TPR, XRD, TGA, and TEM show that elevated calcination temperatures result in increased surface roughness and reduced specific surface areas. The present investigation indicates that the ideal calcination parameters are 900 °C and 3 h. This helps the catalyst work better. This condition gave the best initial activity (54% CH₄ conversion and 61% CO₂ conversion) and the best long-term stability and resistance to carbon deposition. Using MATLAB R2025b (ODE45) for kinetic analysis, it was found that these factors have a big effect on activation energy. Shorter calcination of 1 h gave high initial activity, but it quickly lost its effectiveness. On the other hand, a longer calcination time of 9 h made the material more stable but less able to condition convert because it sintered too much. These results show that it is very important to carefully control the conditions of calcination in order to make long-lasting, high-performance catalysts for making syngas. Moreover, a 20-h DRM stability run of the optimum catalyst exhibited nearly constant activity, highlighting its strong structural integrity and superior ability to alleviate rapid coke formation. Full article

Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To address this, this paper proposes a safety-critical control framework built on a Hamiltonian neural differential model (HDM). The HDM formulates the quadrotor dynamics under a Hamiltonian structure over the $\mathit{SE}\left(3\right)$ manifold, with explicitly optimizable inertia parameters and a neural network-approximated control input matrix. This yields a neural ordinary differential equation (ODE) that is solved numerically for state prediction, while all parameters are trained jointly from data via gradient descent. Unlike black-box models, the HDM incorporates physical priors—such as $\mathit{SE}\left(3\right)$ constraints and energy conservation—ensuring a physically plausible and interpretable dynamics representation. Furthermore, the HDM is reformulated into a control-affine form, enabling controller synthesis via control Lyapunov functions (CLFs) for stability and exponential control barrier functions (ECBFs) for rigorous safety guarantees. Simulations validate the framework’s effectiveness in achieving safe and stable tracking control. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

The rapid development of China’s high-speed railways (HSRs) and the implementation of revenue management policies have promoted the marketization of railway passenger transport, which is mainly reflected in the gradual transformation from a seller’s market dominated by operating companies to a buyer’s market dominated by passenger demand. Passenger travel needs are becoming increasingly diverse. In order to improve the quality of HSR services and attract more passengers, this paper starts from passenger satisfaction and considers the heterogeneity of travel preferences of passengers with different travel distances. Based on the passenger travel data of the Nanning-Guangzhou (NG) HSR line, the K-means clustering method is used to classify passengers into three categories: short-distance, medium-distance, and long-distance travel. A structural equation modeling–multinomial logit (SEM-MNL) model integrating both explicit and latent variables was constructed to analyze passenger travel origin-destination (OD) choices. Stata software was used to estimate passenger preferences for perceived satisfaction functions across different travel distances. Finally, considering constraints such as load factor, departure capacity, and spatiotemporal passenger flow demand, a line planning optimization model was constructed with the goal of minimizing train operating costs and maximizing passenger travel satisfaction. An improved subtraction optimizer algorithm was designed for the solution. Using the NG Line as a case study, the proposed method achieved a reduction in train operating costs while enhancing overall passenger satisfaction. Full article

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics—stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins (“attractors”). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were inferred from cross-sectional population variability rather than longitudinal follow-up, serving as a proof-of-concept approximation of morphologic continuity. Voxel-level perturbation quantified local morphodynamic sensitivity, and proof-of-concept control was explored by adding small inputs to the neural-ODE using both a deterministic controller and a reinforcement learning agent based on soft actor–critic (SAC). Survival analyses (Kaplan–Meier, log-rank, ridge-regularized Cox) assessed associations with outcomes. Results: The learned latent manifold was smooth and clinically organized. Three dominant attractor basins were identified with significant survival stratification (χ² = 31.8, p = 1.3 × 10⁻⁷) in the static model. Dynamic attractor basins derived from neural-ODE endpoints showed modest and non-significant survival differences, confirming that these dynamic labels primarily encode the morphodynamic structure rather than fixed prognostic strata. Dynamic basins inferred from neural-ODE flows were not independently prognostic, indicating that the inferred morphodynamic field captures geometric organization rather than additional clinical risk information. The latent stability index showed a weak but borderline significant negative association with survival (ρ = −0.13 [−0.26, −0.01]; p = 0.0499). In multivariable Cox models, age remained the dominant covariate (HR = 1.30 [1.16–1.45]; p = 5 × 10⁻⁶), with overall C-indices of 0.61–0.64. Voxel-level sensitivity maps highlighted enhancing rims and peri-necrotic interfaces as influential regions. In simulation, deterministic control redirected trajectories toward lower-risk basins (≈57% success; ≈96% terminal distance reduction), while a soft actor–critic (SAC) agent produced smoother trajectories and modest additional reductions in terminal distance, albeit without matching the deterministic controller’s success rate. The learned attractor classes were internally consistent and clinically distinct. Conclusions: Learning a latent attractor landscape links generative AI, dynamical systems theory, and clinical outcomes in GBM. Although limited by the cross-sectional nature of BraTS and modest prognostic gains beyond age, these results provide a mechanistic, controllable framework for tumor morphology in which inferred dynamic attractor-like flows describe latent organization rather than a clinically predictive temporal model, motivating prospective radiogenomic validation and adaptive therapy studies. Full article

This study examines the generalized nonlinear Hunter–Saxton (HS) model: ${\Phi }_{tx}=\Phi {\Phi }_{xx}+\gamma {\Phi }_x^2,\gamma \ne 0,$ that describes the evolution of spatial potential and angular velocity in the vector field of nematic liquid crystals. Closed-form nematicons are derived via the order reduction of the traveling wave ODE. The qualitative structures are analyzed for different values of the nonlinear parameter $\gamma$. The solutions are graphically depicted to discover rich nematicon geometries including parabolic, cuspon, kink, and singular wave structures. A comprehensive dynamic analysis of the reduced nonlinear ordinary system is performed using the phase plane method, which helps to reveal the non-isolated continuity of equilibrium and the role of singular manifolds in shaping the system’s sensitivity and stability. Bifurcation cases are investigated for distinct values of $\gamma$, and various transitions in trajectory geometry and semi-stability features are shown. The novelty appears in the comprehensive integrating of analytic and dynamic characterizations, through global phase and bifurcation analysis, of the generalized HS equation (HSE), which uncovers the control of nonlinear coefficient $\gamma$ in governing the geometry and stability of the nematicons. Also, the analysis confirms the non-chaotic nature of the associated two-dimensional system, compatible with the Poincaré–Bendixson theorem. Full article

Stapedotomy is performed to restore ossicular chain sound transmission by inserting a piston prosthesis that couples the long process of the incus to the oval window, thereby addressing conductive hearing loss associated with otosclerosis. This study investigates the effects of crimping force, prosthesis material, and loop geometry on incus to optimize fixation while minimizing complications such as incudal necrosis. Finite element analyses were performed to quantify interface pressures and von Mises stresses for titanium prostheses with loop-band widths of 0.2, 0.3, and 0.5 mm under crimping forces of 300–500 mN and for polytetrafluoroethylene (PTFE) prostheses with loop outer diameters (OD) of 1.2, 1.4, and 1.8 mm. The analysis results showed that PTFE prostheses generated significantly lower interface pressures and stress compared to titanium. For PTFE prostheses, the equivalent von Mises stresses remained well below the critical threshold, with values ranging from 3.5 MPa up to peaks of approximately 43 MPa depending on the loop’s outer diameter. In contrast, titanium prostheses exhibited a marked dependency on crimping force and band width. At a force of 300 mN, stresses were modest (approximately 16–24 MPa). However, when increasing the force to 400 mN, stresses approached the critical threshold (up to approximately 53 MPa). With crimping forces of 500 mN, especially with band widths greater than 0.3 mm, stresses exceeded the cortical bone strength threshold (approximately 61–64 MPa), indicating an increased risk of mechanical overload and potential incudal necrosis. These findings highlight the importance, in a clinical context, of controlling the crimping force and selecting the material and geometry of the prosthesis to achieve secure coupling while preserving the incus’s structural integrity. Full article

Objectives: The aim of this study was to evaluate the stability of azacitidine (AZA) under clinical storage conditions (room temperature vs. refrigeration) to identify practical protocols that minimize waste and improve cost-effectiveness. Methods: AZA solutions (1 mg/mL) were stored at 23 ± 2 °C or 4 °C. Stability was assessed using a validated high-performance liquid chromatography (HPLC) method. Chromatographic separation was achieved on a Hypersil ODS C₁₈ column (250 mm × 4.6 mm, 5 μm) using an isocratic mobile phase of 50 mM potassium phosphate buffer (pH 7.0)-acetonitrile (98:2, v/v) at a flow rate of 1.0 mL/min, with UV detection at 245 nm and a 20 μL injection volume. The method demonstrated specificity for AZA and its main degradation product (DP), with LOD and LOQ of 12.56 μg/mL and 62.8 μg/mL, respectively. Linearity (R² = 0.9928), precision (RSD% < 5 for mid/high levels), and accuracy (mean recovery 96%) were established. Results: Azacitidine degraded rapidly at room temperature, with >85% loss within 24 h. In contrast, refrigeration at 4 °C significantly delayed degradation, with only ~26% loss observed over the same 24 h period. Chromatographic analysis confirmed the formation of a primary degradation product (tentatively identified as the open-ring hydrolytic species N-(formylamidino)-N′-β-D-ribofuranosylurea based on its chromatographic behavior and literature data), consistent with the known hydrolytic pathway. The applied HPLC-UV method offered an optimal balance of specificity and practicality for monitoring this main degradation trend under clinical storage conditions, distinguishing it from more complex techniques used primarily for structural elucidation. Conclusions: The pronounced instability of reconstituted AZA underscores the critical importance of strict adherence to immediate-use protocols. Refrigeration provides only a limited stability window. Based on our kinetic data, maintaining the reconstituted solution within an acceptable degradation limit (e.g., ≤10% loss) at 4 °C would require administration within a very short timeframe, supporting current handling guidelines to ensure therapeutic efficacy and minimize economic waste. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Motivation: We aim to study the boundary stability and persistence of positive odes in mathematical epidemiology models by importing structural tools from chemical reaction networks. This is largely a review work, which attempts to congregate the fields of mathematical epidemiology (ME), and chemical reaction networks (CRNs), based on several observations. We started by observing that epidemiologic strains, defined as disjoint blocks in either the Jacobian on the infected variables, or as blocks in the next generating matrix (NGM), coincide in most of the examples we studied, with either the set of critical minimal siphons or with the set of minimal autocatalytic sets (cores) in an underlying CRN. We leveraged this to provide a definition of the disease-free equilibrium (DFE) face/infected set as the union of either all minimal siphons, or of all cores (they always coincide in our examples). Next, we provide a proposed definition of ME models, as models which have a unique boundary fixed point on the DFE face, and for which the Jacobian of the infected subnetwork admits a regular splitting, which allows defining the famous next generating matrix. We then define the interaction graph on minimal siphons (IGMS), whose vertices are minimal siphons, and whose edges indicate the existence of reactions producing species in one siphon from species in another. When this graph is acyclic, we say the model exhibits an Acyclic Minimal Siphon Decomposition (AMSD). For AMSD models whose minimal siphons partition the infection species, we show that the NGM is block triangular after permutation, which implies the classical max structure of the reproduction number $R_0$ for multi-strain models. In conclusion, using irreversible reaction networks, minimal siphons and acyclic siphon decompositions, we provide a natural bridge from CRN to ME. We implement algorithms to compute IGMS and detect AMSD in our Epid-CRN Mathematica package (which already contain modules to identify minimal siphons, criticality, drainability, self-replicability, etc.). Finally, we illustrate on several multi-strain ME examples how the block structure induced by AMSD, and the ME reproduction functions, allow expressing boundary stability and persistence conditions by comparing growth numbers to 1, as customary in ME. Note that while not addressing the general Persistence Conjecture mentioned in the title, our work provides a systematic method for deriving boundary instability conditions for a significant class of structured models. Full article