CellularPotts.jl: simulating multiscale cellular models in Julia.

SUMMARY: CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia. AVAILABILITY AND IMPLEMENTATION: The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations.

Rationale: The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care. Objectives: Determine the impact of race-specific equations on African American (AA) versus non-Hispanic White individuals. Methods: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort were conducted, comparing non-Hispanic White (n = 6,766) and AA (n = 3,366) participants for COPD manifestations. Measurements and Main Results: Spirometric classifications using race-specific, multiethnic, and "race-reversed" prediction equations (NHANES [National Health and Nutrition Examination Survey] and Global Lung Function Initiative "Other" and "Global") were compared, as were respiratory symptoms, 6-minute-walk distance, computed tomography imaging, respiratory exacerbations, and St. George's Respiratory Questionnaire. Application of different prediction equations to the cohort resulted in different classifications by stage, with NHANES and Global Lung Function Initiative race-specific equations being minimally different, but race-reversed equations moving AA participants to more severe stages and especially between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0 and preserved ratio impaired spirometry groups. Classification using the established NHANES race-specific equations demonstrated that for each of GOLD stages 1-4, AA participants were younger, had fewer pack-years and more current smoking, but had more exacerbations, shorter 6-minute-walk distance, greater dyspnea, and worse BODE (body mass index, airway obstruction, dyspnea, and exercise capacity) scores and St. George's Respiratory Questionnaire scores. Differences were greatest in GOLD stages 1 and 2. Race-reversed equations reclassified 774 AA participants (43%) from GOLD stage 0 to preserved ratio impaired spirometry. Conclusions: Race-specific equations underestimated disease severity among AA participants. These effects were particularly evident in early disease and may result in late detection of COPD.

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.

BACKGROUND: Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function. METHODS AND FINDINGS: We employed graph modeling on 2,643 COPDGene participants (aged 45 to 80 years, 51.25% female, 35.1% African Americans; enrollment 11/2007-4/2011) with smoking history but normal spirometry at study enrollment to identify variables that are directly linked to future lung function abnormalities. We developed logistic regression and random forest predictive models for distinguishing individuals who maintain lung function from those who decline. Of the 131 variables analyzed, 6 were identified as informative to future lung function abnormalities, namely forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness in a 10 mm radius (Pi10), severe emphysema, age, sex, and height. We investigated whether these features predict individuals leaving GOLD 0 status (normal spirometry according to Global Initiative for Obstructive Lung Disease (GOLD) criteria). Linear models, trained with these features, were quite predictive (area under receiver operator characteristic curve or AUROC = 0.75). Random forest predictors performed similarly to logistic regression (AUROC = 0.7), indicating that no significant nonlinear effects were present. The results were externally validated on 150 participants from Specialized Center for Clinically Oriented Research (SCCOR) cohort (aged 45 to 80 years, 52.7% female, 4.7% African Americans; enrollment: 7/2007-12/2012) (AUROC = 0.89). The main limitation of longitudinal studies with 5- and 10-year follow-up is the introduction of mortality bias that disproportionately affects the more severe cases. However, our study focused on spirometrically normal individuals, who have a lower mortality rate. Another limitation is the use of strict criteria to define spirometrically normal individuals, which was unavoidable when studying factors associated with changes in normalized forced expiratory volume in 1 s (FEV1%predicted) or the ratio of FEV1/FVC (forced vital capacity). CONCLUSIONS: This study took an agnostic approach to identify which baseline measurements differentiate and predict the early stages of lung function decline in individuals with previous smoking history. Our analysis suggests that emphysema affects obstruction onset, while airway predominant pathology may play a more important role in future FEV1 (%predicted) decline without obstruction, and FEF25-75% may affect both.

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking.

Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this paper presents a data-driven, phase-based controller for variable-task walking that uses continuously-variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N=2) show that our data-driven controller 1) features highly-linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.

Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNß production.

MOTIVATION: The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNß). Experimental evidence shows that IFNß signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNß production, an agent-based model is developed to simulate both DNA transfection and viral infection. RESULTS: We show that heterogeneity can enhance IFNß responses during infection. Furthermore, by varying the degree of IFNß stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection. AVAILABILITY AND IMPLEMENTATION: Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nonholonomic Virtual Constraints for Control of Powered Prostheses Across Walking Speeds.

This paper presents a method to design a nonholonomic virtual constraint (NHVC) controller that produces multiple distinct stance-phase trajectories for corresponding walking speeds. NHVCs encode velocity-dependent joint trajectories via momenta conjugate to the unactuated degree(s)-of-freedom of the system. We recently introduced a method for designing NHVCs that allow for stable bipedal robotic walking across variable terrain slopes. This work extends the notion of NHVCs for application to variable-cadence powered prostheses. Using the segmental conjugate momentum for the prosthesis, an optimization problem is used to design a single stance-phase NHVC for three distinct walking speed trajectories (slow, normal, and fast). This stance-phase controller is implemented with a holonomic swing phase controller on a powered knee-ankle prosthesis, and experiments are conducted with an able-bodied user walking in steady and non-steady velocity conditions. The control scheme is capable of representing 1) multiple, task-dependent reference trajectories, and 2) walking gait variance due to both temporal and kinematic changes in user motion.

Design Principles for Compact, Backdrivable Actuation in Partial-Assist Powered Knee Orthoses.

This paper presents the design and validation of a backdrivable powered knee orthosis for partial assistance of lower-limb musculature, which aims to facilitate daily activities in individuals with musculoskeletal disorders. The actuator design is guided by design principles that prioritize backdrivability, output torque, and compactness. First, we show that increasing the motor diameter while reducing the gear ratio for a fixed output torque ultimately reduces the reflected inertia (and thus backdrive torque). We also identify a tradeoff with actuator torque density that can be addressed by improving the motor's thermal environment, motivating our design of a custom Brushless DC motor with encapsulated windings. Finally, by designing a 7:1 planetary gearset directly into the stator, the actuator has a high package factor that reduces size and weight. Benchtop tests verify that the custom actuator can produce at least 23.9 Nm peak torque and 12.78 Nm continuous torque, yet has less than 2.68 Nm backdrive torque during walking conditions. Able-bodied human subjects experiments (N=3) demonstrate reduced quadriceps activation with bilateral orthosis assistance during lifting-lowering, sit-to-stand, and stair climbing. The minimal transmission also produces negligible acoustic noise.

Pathogenesis of Influenza A(H7N9) Virus in Aged Nonhuman Primates.

The avian influenza A(H7N9) virus has caused high mortality rates in humans, especially in the elderly; however, little is known about the mechanistic basis for this. In the current study, we used nonhuman primates to evaluate the effect of aging on the pathogenicity of A(H7N9) virus. We observed that A(H7N9) virus infection of aged animals (defined as age 20-26 years) caused more severe symptoms than infection of young animals (defined as age 2-3 years). In aged animals, lung inflammation was weak and virus infection was sustained. Although cytokine and chemokine expression in the lungs of most aged animals was lower than that in the lungs of young animals, 1 aged animal showed severe symptoms and dysregulated proinflammatory cytokine and chemokine production. These results suggest that attenuated or dysregulated immune responses in aged animals are responsible for the severe symptoms observed among elderly patients infected with A(H7N9) virus.

Design and Validation of a Powered Knee-Ankle Prosthesis with High-Torque, Low-Impedance Actuators.

We present the design of a powered knee-ankle prosthetic leg, which implements high-torque actuators with low-reduction transmissions. The transmission coupled with a high-torque and low-speed motor creates an actuator with low mechanical impedance and high backdrivability. This style of actuation presents several possible benefits over modern actuation styles in emerging robotic prosthetic legs, which include free-swinging knee motion, compliance with the ground, negligible unmodeled actuator dynamics, less acoustic noise, and power regeneration. Benchtop tests establish that both joints can be backdriven by small torques (~1-3 Nm) and confirm the small reflected inertia. Impedance control tests prove that the intrinsic impedance and unmodeled dynamics of the actuator are sufficiently small to control joint impedance without torque feedback or lengthy tuning trials. Walking experiments validate performance under the designed loading conditions with minimal tuning. Lastly, the regenerative abilities, low friction, and small reflected inertia of the presented actuators reduced power consumption and acoustic noise compared to state-of-art powered legs.

Extremum Seeking Control for Model-Free Auto-Tuning of Powered Prosthetic Legs.

This paper proposes an extremum seeking controller (ESC) for simultaneously tuning the feedback control gains of a knee-ankle powered prosthetic leg using continuous-phase controllers. Previously, the proportional gains of the continuous-phase controller for each joint were tuned manually by trial-and-error, which required several iterations to achieve a balance between the prosthetic leg tracking error performance and the user's comfort. In this paper, a convex objective function is developed, which incorporates these two goals. We present a theoretical analysis demonstrating that the quasi-steady-state value of the objective function is independent of the controller damping gains. Furthermore, we prove the stability of error dynamics of continuous-phase controlled powered prosthetic leg along with ESC dynamics using averaging and singular perturbation tools. The developed cost function is then minimized by ESC in real-time to simultaneously tune the proportional gains of the knee and ankle joints. The optimum of the objective function shifts at different walking speeds, and our algorithm is suitably fast to track these changes, providing real-time adaptation for different walking conditions. Benchtop and walking experiments verify the effectiveness of the proposed ESC across various walking speeds.

Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback.

Cyclic GMP-AMP synthase (cGAS) has recently been identified as the primary protein that detects cytosolic double stranded DNA to invoke a type I interferon response. The cGAS pathway is vital in the recognition of DNA encoded viruses as well as self-DNA leaked from the nucleus of damaged cells. Currently, the dynamics regulating the cGAS pathway are poorly understood; limiting our knowledge of how DNA-induced immune responses are regulated. Using systems biology approaches, we formulated a mathematical model to describe the dynamics of this pathway and examine the resulting system-level emergent properties. Unknown model parameters were fit to data compiled from literature using a Parallel Tempering Markov Chain Monte Carlo (PT-MCMC) approach, resulting in an ensemble of parameterized models. A local sensitivity analysis demonstrated that parameter sensitivity trends across model ensembles were independent of the select parameterization. An in-silico knock-down of TREX1 found that the interferon response is highly robust, showing that complete inhibition is necessary to induce chemical conditions consistent with chronic inflammation. Lastly, we demonstrate that the model recapitulates interferon expression data resulting from small molecule inhibition of cGAS. Overall, the importance of this model is exhibited in its capacity to identify sensitive components of the cGAS pathway, generate testable hypotheses, and confirm experimental observations.

Minimizing Energy Consumption and Peak Power of Series Elastic Actuators: A Convex Optimization Framework for Elastic Element Design.

Compared to rigid actuators, series elastic actuators (SEAs) offer a potential reduction of motor energy consumption and peak power, though these benefits are highly dependent on the design of the torque-elongation profile of the elastic element. In the case of linear springs, natural dynamics is a traditional method for this design, but it has two major limitations-arbitrary load trajectories are difficult or impossible to analyze and it does not consider actuator constraints. Parametric optimization is also a popular design method that addresses these limitations, but solutions are only optimal within the space of the parameters. To overcome these limitations, we propose a nonparametric convex optimization program for the design of the nonlinear elastic element that minimizes energy consumption and peak power for an arbitrary periodic reference trajectory. To obtain convexity, we introduce a convex approximation to the expression of peak power; energy consumption is shown to be convex without approximation. The combination of peak power and energy consumption in the cost function leads to a multiobjective convex optimization framework that comprises the main contribution of this paper. As a case study, we recover the elongation-torque profile of a cubic spring, given its natural oscillation as the reference load. We then design nonlinear SEAs for an ankle prosthesis that minimize energy consumption and peak power for different trajectories and extend the range of achievable tasks when subject to actuator constraints.

Decentralized Passivity-Based Control With a Generalized Energy Storage Function for Robust Biped Locomotion.

This paper details a decentralized passivity-based control (PBC) to improve the robustness of biped locomotion in the presence of gait-generating external torques and parametric errors in the biped model. Previous work demonstrated a passive output for biped systems based on a generalized energy that, when directly used for feedback control, increases the basin of attraction and convergence rate of the biped to a stable limit cycle. This paper extends the concept with a theoretical framework to address both uncertainty in the biped model and a lack of sensing hardware, by allowing the designer to neglect arbitrary states and parameters in the system. This framework also allows the control to be implemented on wearable devices, such as a lower limb exoskeleton or powered prosthesis, without needing a model of the user's dynamics. Simulations on a six-link biped model demonstrate that the proposed control scheme increases the convergence rate of the biped to a walking gait and improves the robustness to perturbations and to changes in ground slope.

PET CT Identifies Reactivation Risk in Cynomolgus Macaques with Latent M. tuberculosis.

Mycobacterium tuberculosis infection presents across a spectrum in humans, from latent infection to active tuberculosis. Among those with latent tuberculosis, it is now recognized that there is also a spectrum of infection and this likely contributes to the variable risk of reactivation tuberculosis. Here, functional imaging with 18F-fluorodeoxygluose positron emission tomography and computed tomography (PET CT) of cynomolgus macaques with latent M. tuberculosis infection was used to characterize the features of reactivation after tumor necrosis factor (TNF) neutralization and determine which imaging characteristics before TNF neutralization distinguish reactivation risk. PET CT was performed on latently infected macaques (n = 26) before and during the course of TNF neutralization and a separate set of latently infected controls (n = 25). Reactivation occurred in 50% of the latently infected animals receiving TNF neutralizing antibody defined as development of at least one new granuloma in adjacent or distant locations including extrapulmonary sites. Increased lung inflammation measured by PET and the presence of extrapulmonary involvement before TNF neutralization predicted reactivation with 92% sensitivity and specificity. To define the biologic features associated with risk of reactivation, we used these PET CT parameters to identify latently infected animals at high risk for reactivation. High risk animals had higher cumulative lung bacterial burden and higher maximum lesional bacterial burdens, and more T cells producing IL-2, IL-10 and IL-17 in lung granulomas as compared to low risk macaques. In total, these data support that risk of reactivation is associated with lung inflammation and higher bacterial burden in macaques with latent Mtb infection.

Continuous-Phase Control of a Powered Knee-Ankle Prosthesis: Amputee Experiments Across Speeds and Inclines.

Control systems for powered prosthetic legs typically divide the gait cycle into several periods with distinct controllers, resulting in dozens of control parameters that must be tuned across users and activities. To address this challenge, this paper presents a control approach that unifies the gait cycle of a powered knee-ankle prosthesis using a continuous, user-synchronized sense of phase. Virtual constraints characterize the desired periodic joint trajectories as functions of a phase variable across the entire stride. The phase variable is computed from residual thigh motion, giving the amputee control over the timing of the prosthetic joint patterns. This continuous sense of phase enabled three transfemoral amputee subjects to walk at speeds from 0.67 to 1.21 m/s and slopes from -2.5 to +9.0 deg. Virtual constraints based on task-specific kinematics facilitated normative adjustments in joint work across walking speeds. A fixed set of control gains generalized across these activities and users, which minimized the configuration time of the prosthesis.

Underactuated Potential Energy Shaping with Contact Constraints: Application to a Powered Knee-Ankle Orthosis.

Body-weight support (i.e., gravity compensation) is an effective clinical tool for gait rehabilitation after neurological impairment. Body-weight supported training systems have been developed to help patients regain mobility and confidence during walking, but conventional systems constrain the patient's treatment in clinical environments. We propose that this challenge could be addressed by virtually providing patients with bodyweight support through the actuators of a powered orthosis (or exoskeleton) utilizing potential energy shaping control. However, the changing contact conditions and degrees of underactuation encountered during human walking present significant challenges to consistently matching a desired potential energy for the human in closed loop. We therefore derive a generalized matching condition for shaping Lagrangian systems with holonomic contact constraints. By satisfying this matching condition for four phases of gait, we derive passivity-based control laws to achieve virtual body-weight support through a powered knee-ankle orthosis. We demonstrate beneficial effects of virtual body-weight support in simulations of a human-like biped model, indicating the potential clinical value of this proposed control approach.

Toward Unified Control of a Powered Prosthetic Leg: A Simulation Study.

This brief presents a novel control strategy for a powered knee-ankle prosthesis that unifies the entire gait cycle, eliminating the need to switch between controllers during different periods of gait. A reduced-order Discrete Fourier Transformation (DFT) is used to define virtual constraints that continuously parameterize periodic joint patterns as functions of a mechanical phasing variable. In order to leverage the provable stability properties of Hybrid Zero Dynamics (HZD), hybrid-invariant Bézier polynomials are converted into unified DFT virtual constraints for various walking speeds. Simulations of an amputee biped model show that the unified prosthesis controller approximates the behavior of the original HZD design under ideal scenarios and has advantages over the HZD design when hybrid invariance is violated by mismatches with the human controller. Two implementations of the unified virtual constraints, a feedback linearizing controller and a more practical joint impedance controller, produce similar results in simulation.

Stable, Robust Hybrid Zero Dynamics Control of Powered Lower-Limb Prostheses.

To improve the quality of life for lower-limb amputees, powered prostheses are being developed. Advanced control schemes from the field of bipedal robots, such as hybrid zero dynamics (HZD), may provide great performance. HZD-based control specifies the motion of the actuated joints using output functions to be zeroed, and the required torques are calculated using input-output linearization. For one-step periodic gaits, there is an analytic metric of stability. To apply HZD-based control on a powered prosthesis, several modifications must be made. Because the prosthesis and amputee are only connected via the socket, the prosthesis controller does not have access to the full state of the biped, which decentralizes the form of the input-output linearization. The differences between the amputated and contralateral sides result in a two-step periodic gait, which requires the orbital stability metric to be extended. In addition, because human gait is variable, the prosthesis controller must be robust to continuous moderate perturbations. This robustness is proved using local input-to-state stability and demonstrated with simulations of an above-knee amputee model.

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking.

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and [Formula: see text] robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.