Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network.

BACKGROUND: For the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances. METHODS: A control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety. RESULTS: Experiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements. CONCLUSIONS: The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.

Nonlinearity and anthocyanin colour expression: A mathematical analysis of anthocyanin association kinetics and equilibria.

Anthocyanins are polyphenolic compounds that provide pigmentation in plants as reflected by pH-dependent structural transformations between the red flavylium cation, purple quinonoidal base, blue quinonoidal anion, colourless hemiketal, and pale yellow chalcone species. Thermodynamically stable conditions of hydrated plant cell vacuoles in vivo correspond to the colourless hemiketal, yet anthocyanin colour expression appears in an important variety of hues within plant organs such as flowers and fruit. Moreover, anthocyanin colour from grape berries is significant in red winemaking processes as it plays a crucial role in determining red wine quality. Here, nonlinear ordinary differential equations were developed to represent the evolution in concentration of various anthocyanin species in both monomeric (chemically reactive) and self-associated (temporally stable) forms for the first time, and simulations were verified experimentally. Results indicated that under hydrating conditions, anthocyanin pigmentation is preserved by self-association interactions, based on pigmented monomeric anthocyanins experiencing colour loss whereas colour-stable self-associated anthocyanins increase in concentration nonlinearly over time. In particular, self-association of the flavylium cation and the quinonoidal base was shown to influence colour expression and stability within Geranium sylvaticum flower petals and Vitis vinifera grape skins. This study ultimately characterises fundamental mechanisms of anthocyanin stabilisation and generates a quantitative framework for anthocyanin-containing systems.

Nonlinear association of fibrinogen levels with functional prognosis in patients with acute ischemic stroke: a prospective cohort study.

OBJECTIVE: Fibrinogen, essential in primary hemostasis, platelet aggregation, and leukocyte-endothelial interactions, is also associated with a heightened risk of acute ischemic stroke (AIS). However, its influence on AIS patient outcomes is unclear. This study examines the correlation between fibrinogen levels and the risk of unfavorable outcomes three months post-AIS. METHODS: This is a secondary analysis of a prospective cohort study conducted in Korea. The sample consisted of 1851 AIS patients who received treatment at a Korean hospital between January 2010 and December 2016. Statistical models were established to understand the relationship between fibrinogen levels(mg/dL) and unfavorable outcomes(mRs ≥ 3), including logistic regression models, Generalized Additive Models (GAM), and smooth curve fitting (penalized splines). The log-likelihood ratio test has been utilized to evaluate the best fit. To ensure the robustness of the results, sensitivity analyses were conducted by reanalyzing the relationship after excluding participants with TG > 200 mg/dl and BMI > 25 kg/m2. Subgroup analyses were also performed to assess whether influencing factors modify the association between fibrinogen levels and unfavorable outcomes. RESULTS: After adjusting for multiple covariates including age, BMI, sex, LDL-c, TG, HGB, HDL-c, BUN, FPG, ALB, PLT, AF, hypertension, smoking, DM, mRs score at admission, the binary logistic regression model demonstrated revealed a significant positive association between fibrinogen levels and the risk of unfavorable outcomes in AIS patients (OR = 1.215, 95% CI: 1.032-1.429, p = 0.019). Sensitivity analyses supported these findings, with similar ORs observed in subsets of patients with TG < 200 mg/dL (OR = 1.221, 95% CI: 1.036-1.440) and BMI < 25 kg/m2 (OR = 1.259, 95% CI: 1.051-1.509). Additionally, the relationship between fibrinogen levels and outcomes was nonlinear, with a critical threshold of 2.74 g/L. Below the inflection point, the OR for unfavorable outcomes was 0.666 ((95% CI: 0.360, 1.233, p = 0.196), whereas above it, the OR increased to 1.374 (95% CI: 1.138, 1.659). CONCLUSIONS: This study has provided evidence of a positive and nonlinear correlation between fibrinogen levels and 3-month poor functional outcomes in patients with AIS. When fibrinogen levels exceeded 2.74 g/L, a significant and positive association was observed with the risk of poor outcomes. This study provides a further reference for optimizing rehabilitation exercises and facilitating clinical counseling in patients with acute ischemic stroke.

Spatially modulated ablation driven by chaotic attractors in human lung epithelial cancer cells.

A significant modification in photoinduced energy transfer in cancer cells is reported by the assistance of a dynamic modulation of the beam size of laser irradiation. Human lung epithelial cancer cells in monolayer form were studied. In contrast to the quantum and thermal ablation effect promoted by a standard focused Gaussian beam, a spatially modulated beam can caused around 15% of decrease in the ablation threshold and formation of a ring-shaped distribution of the photothermal transfer effect. Optical irradiation was conducted in A549 cells by a 532 nm single-beam emerging from a Nd:YVO4 system. Ablation effects derived from spatially modulated convergent waves were controlled by an electrically focus-tunable lens. The proposed chaotic behavior of the spatial modulation followed an Arneodo chaotic oscillator. Fractional dynamic thermal transport was analyzed in order to describe photoenergy in propagation through the samples. Immediate applications of chaos theory for developing phototechnology devices driving biological functions or phototherapy treatments can be considered.

Modeling Nonlinear Dynamics of Functionalization Layers: Enhancing Gas Sensor Sensitivity for Piezoelectrically Driven Microcantilever.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance (f-d) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer.

BACKGROUND: Preclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data. METHODS: Nonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME. RESULTS: Cox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8+ Ki67 MFI, as well as DD7 and cluster specific Natural Killer cell density in RT treated mice. CONCLUSION: Application of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

The butterfly effect in oral and maxillofacial surgery: Understanding and applying chaos theory and complex systems principles.

The present paper provides a historical context for chaos theory, originating in the 1960s with Edward Norton Lorenz's efforts to predict weather patterns. It introduces chaos theory, fractal geometry, nonlinear dynamics, and the butterfly effect, highlighting their exploration of complex systems. The authors aim to bridge the gap between chaos theory and oral and maxillofacial surgery (OMFS) through a literature review, exploring its applications and emphasizing the prevention of minor deviations in OMFS to avoid significant consequences. A comprehensive literature review was conducted on PubMed, Web of Science, and Google Scholar databases. The selection process adhered to the PRISMA-ScR guidelines and Leiden Manifesto principles. Articles focusing on chaos theory principles in health sciences, published in the last two decades, were included. The review encompassed 37 articles after screening 386 works. It revealed applications in outcome variation, surgical planning, simulations, decision-making, and emerging technologies. Potential applications include predicting infections, malignancies, dental fractures, and improving decision-making through disease prediction systems. Emerging technologies, despite criticisms, indicate advancements in AI integration, contributing to enhanced diagnostic accuracy and personalized treatment strategies. Chaos theory, a distinct scientific framework, holds potential to revolutionize OMFS. Its integration with advanced techniques promises personalized, less traumatic surgeries and improved patient care. The interdisciplinary synergy of chaos theory and emerging technologies presents a future in which OMFS practices become more efficient, less traumatic, and achieve a level of precision never seen before.

Dynamic analysis of slant cracked rotor system considering nonlinear oil film force.

In this paper, considering the combined effects of nonlinear oil film forces and cracks on the rotor-bearing system, the differential equations of motion with 4 degrees of freedom are established by Lagrangian method. Then, the Lundgren-Kutta method is used to solve them and the results of the model are compared with the experimental data. The study demonstrate that the cracked rotor-bearing system is relatively stable at subcritical speeds, mostly in the period-1 motion. But near 1/3 of the critical speed, there is an inner loop in its whirl orbit and a significant increase in the 2x frequency component. When the system speed rises to the region near 1/2 of the critical speed, though the bifurcation motion and a relatively high 2x frequency can be observed, there are no other reliable fault characteristics. The study proves that the rotor crack fault diagnosis method based on the whirl orbits is convincing for slant cracked rotors.

Comparing linear and non-linear models to estimate the appropriate cochlear implant electrode array length-are current methods precise enough?

PURPOSE: In cochlear implantation with flexible lateral wall electrode arrays, a cochlear coverage (CC) range between 70% and 80% is considered ideal for optimal speech perception. To achieve this CC, the cochlear implant (CI) electrode array has to be chosen according to the individual cochlear duct length (CDL). Here, we mathematically analyzed the suitability of different flexible lateral wall electrode array lengths covering between 70% and 80% of the CDL. METHODS: In a retrospective cross-sectional study preoperative high-resolution computed tomography (HRCT) from patients undergoing cochlear implantation was investigated. The CDL was estimated using an otosurgical planning software and the CI electrode array lengths covering 70-80% of the CDL was calculated using (i) linear and (ii) non-linear models. RESULTS: The analysis of 120 HRCT data sets showed significantly different model-dependent CDL. Significant differences between the CC of 70% assessed from linear and non-linear models (mean difference: 2.5 mm, p < 0.001) and the CC of 80% assessed from linear and non-linear models (mean difference: 1.5 mm, p < 0.001) were found. In up to 25% of the patients none of the existing flexible lateral wall electrode arrays fit into this range. In 59 cases (49,2%) the models did not agree on the suitable electrode arrays. CONCLUSIONS: The CC varies depending on the underlying CDL approximation, which critically influences electrode array choice. Based on the literature, we hypothesize that the non-linear method systematically overestimates the CC and may lead to rather too short electrode array choices. Future studies need to assess the accuracy of the individual mathematical models.

Adaptive Virotherapy Strategy for Organism With Constrained Input Using Medicine Dosage Regulation Mechanism.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

Metastasis Models: Thermodynamics and Complexity.

The thermodynamic formalism of nonequilibrium systems together with the theory of complex systems and systems biology offer an appropriate theoretical framework to explain the complexity observed at the macroscopic level in physiological phenomena. In turn, they allow the establishment of an appropriate conceptual and operational framework to address the study of phenomena such as the emergence and evolution of cancer.This chapter is organized as follows: In Subheading 1, an integrated vision of these disciplines is offered for the characterization of the emergence and evolution of cancer, seen as a nonlinear dynamic system, temporally and spatially self-organized out of thermodynamic equilibrium. The development of the various mathematical models and different techniques and approaches used in the characterization of cancer metastasis is presented in Subheading 2. Subheading 3 is devoted to the time course of cancer metastasis, with particular emphasis on the epithelial-mesenchymal transition (EMT henceforth) as well as chronotherapeutic treatments. In Subheading 4, models of the spatial evolution of cancer metastasis are presented. Finally, in Subheading 5, some conclusions and remarks are presented.

Systems Biology Modeling of Cancer Nonlinear Dynamics.

Not unlike the climate or what holds the galaxies and planetary motions together, cancer biology has an intrinsic nonlinear dynamic. In this overview we will outline how to connect temporal measurements of a nonlinear dynamical and unstable complex system, such as cancer, with well-established engineering methods, old and new, that are applied in linear dynamical systems.This proof-of-concept is therapeutically relevant in the development of new means to treat or control human cancer by either adding an appropriate external "damping" or a "forcing" term, or by a "control" actuator such that its nonlinear dynamic is steered to a spiral stably into zero forever as a sink attractor.

Modeling and analysis of a multilayer solid tumour with cell physiological age and resource limitations.

We study an avascular spherical solid tumour model with cell physiological age and resource constraints in vivo. We divide the tumour cells into three components: proliferating cells, quiescent cells and dead cells in necrotic core. We assume that the division rate of proliferating cells is nonlinear due to the nutritional and spatial constraints. The proportion of newborn tumour cells entering directly into quiescent state is considered, since this proportion can respond to the therapeutic effect of drug. We establish a nonlinear age-structured tumour cell population model. We investigate the existence and uniqueness of the model solution and explore the local and global stabilities of the tumour-free steady state. The existence and local stability of the tumour steady state are studied. Finally, some numerical simulations are performed to verify the theoretical results and to investigate the effects of different parameters on the model.

Koopman-based Impulsive Model Predictive Control of BCG Immunotherapy.

Bacillus of Calmette and Guerin (BCG) is the most effective immunologic treatment for non-muscle-invasive bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG treatment is limited by the dosing scheme, which is difficult to design due to nonlinear dynamics and constraints in the pharmacodynamic model. Here we present a computational method that combines linearization, impulsive control, and constrained optimization to design optimal drug dosing. We do so by first adopting Koopman theory to map the nonlinear pharmacodynamic model into linear space. Then we use model predictive control to design drug dosing schemes based on the transformed linear model with impulsive drug instillation, constrained by drug concentration. With this pipeline, we find that the Koopman-based linear system has almost identical dynamic behaviors to the original model based on numerical simulations. Also, the designed drug doses stay within the constraints and cancerous cell proliferation is effectively suppressed by driving the uninfected tumor cell population to a descending reference trajectory. Robustness tests are performed to show the proposed controller is robust to a certain level of model uncertainty. The method is expected to be generalized to the design of other model-based drug dosing schemes because of its optimality, impulsivity, and linearity.

Nonlinear dynamics of estrogen receptor-positive breast cancer integrating experimental data: A novel spatial modeling approach.

Oncology research has focused extensively on estrogen hormones and their function in breast cancer proliferation. Mathematical modeling is essential for the analysis and simulation of breast cancers. This research presents a novel approach to examine the therapeutic and inhibitory effects of hormone and estrogen therapies on the onset of breast cancer. Our proposed mathematical model comprises a nonlinear coupled system of partial differential equations, capturing intricate interactions among estrogen, cytotoxic T lymphocytes, dormant cancer cells, and active cancer cells. The model's parameters are meticulously estimated through experimental studies, and we conduct a comprehensive global sensitivity analysis to assess the uncertainty of these parameter values. Remarkably, our findings underscore the pivotal role of hormone therapy in curtailing breast tumor growth by blocking estrogen's influence on cancer cells. Beyond this crucial insight, our proposed model offers an integrated framework to delve into the complexity of tumor progression and immune response under hormone therapy. We employ diverse experimental datasets encompassing gene expression profiles, spatial tumor morphology, and cellular interactions. Integrating multidimensional experimental data with mathematical models enhances our understanding of breast cancer dynamics and paves the way for personalized treatment strategies. Our study advances our comprehension of estrogen receptor-positive breast cancer and exemplifies a transformative approach that merges experimental data with cutting-edge mathematical modeling. This framework promises to illuminate the complexities of cancer progression and therapy, with broad implications for oncology.

Optimizing ruminant nutrition: Insights from a comprehensive analysis of silage composition and in vitro gas production dynamics using nonlinear models.

Achieving sustainable livestock management necessitates optimizing animal production while minimizing environmental impact. To achieve this, feed efficiency must be enhanced, and nutrition blueprints must be understood. In ruminant nutrition, this is of paramount importance, as it exposes degradation kinetics and nutritional benchmarks, allowing feed management and formulations to be more ecologically balanced. Previous research efforts have focused on exploring the relationship between a restricted set of nutrient parameters and the in vitro gas production dynamics. In the current study, an extensive dataset derived from freeze-dried kefir culture treated white clover silage was used to examine intricate relationships between eight nonlinear models and diverse variables. This dataset contains in vitro gas production data along with nutritional composition, microbial populations, fermentation quality, digestibility, mineral concentration, and fatty acid profiles. Through rigorous application of mathematical models, the performance in capturing gas production dynamics was critically assessed. Among these, the Michaelis‒Menten (MM) and Mitscherlich (MIT) models fit the data well and offer superior predictions of gas production dynamics. Asymptotic gas volume was negatively correlated with crude protein content, emphasizing the influence of protein on gas production. Fiber composition plays a significant role in fermentation kinetics, as evidenced by significant correlations between degradation rate constant and crude protein concentrations. The degradation rate constant of insoluble fraction exhibited significant positive correlations with crude protein and neutral detergent fiber contents. Moreover, mineral content had significant effects on gas production dynamics. Zinc content showed a strong and significant positive correlation with the gas production rate coefficient, underscoring its crucial role in enhancing microbial activity. Conversely, calcium content displayed a significant but weak negative correlation with the final asymptotic gas volume, indicating its potential to modulate gas production. In summury, this study provides detailed insights into the intricate relationship between mathematical models and various variables in rumen fermentation. The MM and MIT models have proven to be robust tools, offering nuanced perspectives on gas production dynamics. These findings pave the path for improving sustainable ruminant nutritional practices and refining feed management strategies.

Sunshine duration and solar radiation contributed to severe Bell's palsy: An 11-year time series analysis based on a distributed lag non-linear model model.

Although previous studies have suggested that meteorological factors are associated with Bell's palsy, articles on this topic are rare and the results are inconsistent. We aim to reveal the relationship between exposure to different meteorological factors and the onset of severe Bell's palsy (SBP) with daily data. A case-crossover study based on time-series data was applied, and the minimum risk value of each climatic factor was set as the reference value. We fitted a distributed lag non-linear model (DLNM) which applied quasi-Poisson regression to evaluate the exposure-response association and the lag-response association of meteorological factors on the occurrence of SBP. The mode value and per-decile interval value of each meteorological factor were all included in the analysis. Sensitivity analyses were conducted to test the robustness of results. A total of 863 SBP patients (474 males and 389 females) from 7 hospitals in the Shenzhen Futian District were selected from January 2009 to February 2020. The highest relations effect was tested in the cumulative exposure-response result shown as follows; mean temperature at the minimum value 15.3°C with RR of 10.370 (1.557-69.077) over lag 0 to 13; relative humidity at the 30th value 71% with RR of 8.041 (1.016-63.616) over lag 0 to 14; wind speed at the 90th value 31 (0.1 m/s) with RR of 1.286 (1.038-1.593) over lag 0; mean air pressure at the 30th value 1001.4 (pa) with RR of 9.052 (1.039-78.858) over lag 0 to 5; visibility at the 80th value 26.5 (km) with RR of 1.961 (1.005-1.423) over lag 0 to 2; average total cloud cover at the max value 100 (%) with RR 1.787 (1.014-3.148) over lag 0 to 2; sunshine duration at the 10th value 0.1 (h) with RR of 4.772 (1.018-22.361); daily evaporation shows no relationship in the cumulative result; daily average solar radiation at the minimum value 0 (W/m2) with RR of 5.588 (1.184-26.382). There is a relationship between wind speed and the onset of SBP, while mean air pressure, visibility, and average total cloud cover, especially sunshine duration and solar radiation which showed a strong effect, may be associated with severe clinical symptoms of SBP. Mean temperature and relative humidity may affect the course of SBP.

The Prediction of Tumorigenesis Onset Using Parameters from Chaotic Attractor Models.

Tumorigenesis can be modeled as a system of chaotic, nonlinear differential equations. From the analysis of these equations in state space, the onset of tumorigenesis can be predicted based on the status of parameters from the model.

Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer.

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.

A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis.

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.