Time Trajectories Research Articles

Optimal blood pressure values to reduce arterial stiffness, an analysis from UK Biobank

Abstract Introduction Increased arterial stiffness is a powerful independent risk factor for adverse cardiovascular events. While pulse wave velocity has been used extensively as a biomarker of vascular health reflecting functional and structural arterial changes, its use in clinical practice was limited. The Arterial Stiffness Index (ASI) simplifies the assessment of arterial stiffness using a pulse waveform obtained at the finger with an infrared sensor with BP measured using an automated device. Age and raised blood pressure (BP) are major contributors to increased arterial stiffness, but time trajectories of ASI in the general population and optimal BP values to reduce arterial stiffening are scarcely known. Purpose To establish temporal changes of ASI and identify BP values associated with longer-term improvement of arterial stiffness. Methods The study analysed data from UK Biobank (baseline n=154,684, median age 54, interquartile range (IQR) [40-72] years; 52% female; 91% white, 28% hypertension). ASI was measured using PulseTrace PCA2 device (CareFusion, USA) at baseline with measurements repeated in 4617 individuals after a median follow up of 3.0, IQR [2.8-3.3] years. BP was measured during the same time points. Multivariable (adjusted for age, sex, diabetes, and use of BP-lowering drugs) linear regression was used to determine the association of BP with baseline and the association of BP and ASI changes during follow up. ROC analysis was used to determine BP values associated with improvement (reduction) of ASI during follow up. Continuous data are presented as median [IQR] and compared using Wilcoxon test. R software packages were used for analyses. Results At baseline, ASI (m/sec) was higher in those with systolic BP (SBP) >140 mmHg (9.60 [7.15-11.85] vs. 8.67 [6.77-10.70] for SBP ≤140 mmHg, p<0.001) and in those with diastolic BP (DBP) >90 mmHg (9.90 [7.48-12.16] vs 8.81 [6.78-10.90] for DBP ≤90 mmHg, p<0.001). Both SBP and DBP were independently associated with ASI at baseline (B±SE 0.08±0.02 and 0.79±0,29m, respectively; p<0.001 for both). During follow up, the population level SBP remained unchanged (change 0 [-8 - 8] mmHg), with some reduction in DBP (-1.0 [-6.0 - 3.5]) mmHg. SBP reduced in 2234 (48%) and DBP in 2517 (55%). ASI increased by 0.56 [-1.46 - 2.78] m/s, with ASI reduction observed in 1935 (42%). An increase in DBP but not in SBP was independently associated with an increase in ASI (B±SE DBP 0.04±0.01, p<0.001; SBP 0.007±0.006, p=0.22; DBP 0.04±0.007, p<0.001). The baseline BP was predictive of the magnitude of changes in ASI: SBP 132 mmHg (AUC 0.53, p=0.001) and DBP 83 mmHg (AUC 0.54, p<0.001). Conclusions Increased arterial stiffness is associated with higher BP, particularly with higher DBP. Raised ASI is reversible, providing BP is adequately controlled with optimal BP being <132/83 mmHg.

The concept map as a tool for analyzing museum objects

AbstractThis paper discusses using the concept map to study museum objects. The tool's original purpose, which was created in the 1970s by Joseph Novak, was to graphically represent the construction of knowledge of scientific topics by learners, particularly children. Concept maps start from the premise that new knowledge is always built on previous knowledge and allow the representation, organization, and relationship between concepts, which are seen as the primary elements of knowledge. Novak recognized that its potential is insufficiently explored and encouraged its application in different disciplines and fields that deal with concepts and their relationships. This incentive led the Museum of Astronomy and Related Sciences (Rio de Janeiro, Brazil) to adopt the concept map on an experimental basis to analyze objects in its collection. At first, the tool sought to emphasize the conceptual potential of museum objects and shed more light on their trajectory in time and space and the numerous possible connections with events, institutions, concepts, people, and other objects. In the initial experimental phase, the maps were included in papers presented at academic events and discussed with professionals dedicated to science and technology collections and other specialists in Museology and Information Science. In the second stage, maps began to be produced in greater numbers and on demand, focusing on objects selected for a temporary exhibition. Based on the assumption that museum objects are both unique and representative of a class of objects that share the same name and function, the maps constructed were based on Ingetraut Dahlberg's Theory of Concept, which distinguishes between general and individual objects and concepts.

Aging-dependent loss of functional connectivity in a mouse model of Alzheimer's disease and reversal by mGluR5 modulator.

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

Development of auditory cognition in 5- to 10-year-old children: Focus on speech-in-babble-noise perception.

Speech-in-noise perception is consistently reported to be impaired in learning disorders, which stresses the importance of documenting its developmental course in young children. In this cross-sectional study, ninety children (41 females, 5.5-11.6 years old) and nineteen normal-hearing adults (15 females, 20-30 years old) were tested with a newly developed closed-set speech perception in babble-noise test, combining two levels of phonological difficulty and two noise levels. Results showed that speech-in-babble-noise perception takes a definite maturation step around 7 years of age (d = 1.17, grade effect) and is not mature at 10 years of age when compared to young adults (d = 0.94, group effect). Developmental trajectories of both accuracy and response times were evaluated, with influences of psycholinguistic factors, to foster the development of adequate screening tests.

Redefinition for Fundamental Characteristics of Waterway Traffic Stream Considering Vessel Displacement

As the basis for characterizing traffic conditions in waterways, fundamental characteristics of waterway traffic streams are of great practical significance in ensuring traffic safety and improving navigation efficiency. In the study of the fundamental characteristics of waterway traffic flow, although some scholars consider the length or area of the vessel, few scholars take the displacement of the vessel into account and make light of the influence of the three-dimensional size of the vessel. This paper proposes a method for defining the fundamental characteristics of a waterway traffic stream considering vessel size. This method defines fundamental characteristics in terms of vessel displacement and quantifies flow, density, and speed based on vessel trajectories in time–space regions. This study selects a unidirectional channel in the south trough waters of Shanghai Harbor for a case study and draws the fundamental diagram of the waterway traffic stream while considering vessel number and displacement. The comparison result shows that the definition considering vessel displacement can more accurately reflect the actual traffic condition of the selected channel. Finally, based on the flow–density subgraph of the waterway traffic stream (measured by vessel displacement), this paper constructs a traffic stream model and derives critical parameters. The definition proposed in this study effectively characterizes how vessels occupy the time–space resources of waterways, revealing the inherent mechanisms governing waterway traffic stream and, thus, enhancing accuracy in describing fundamental relationships among waterway stream characteristics. The outcome of this research underlies how the waterway traffic stream is measured, operated, and managed to ensure safety and productivity.

Cooperative Adaptive Cruise Control Platoon Fleet Formation Model Considering Efficiency and Stability

In the existing formation model, vehicles in the same lane or adjacent lane are regarded as the structure, and the driving behavior of vehicles is studied from the perspectives of safety, speed consistency, and stability, and the speed control model is proposed from the perspective of vehicles themselves, to obtain a stable fleet with the same distance and speed. However, in this process, the initial condition of the vehicle, the traffic flow environment, and the efficiency of the fleet formation are less considered. Therefore, based on summarizing the existing fleet building model, this paper puts forward the rapid construction model and algorithm of a cooperative adaptive cruise control platoon fleet. One of the important goals of forming a team is to enter the team with the smoothest trajectory in the shortest time. Therefore, this chapter studies the trajectory optimization of the vehicle formation process from the perspective of vehicle dynamics.

Real-time Traffic Conflict Prediction at Signalized Intersections Using Vehicle Trajectory Data and Deep Learning

Real-time conflict prediction at signalized intersections is crucial for urban road safety management. This study developed a real-time conflict prediction framework for signalized intersections using video data real-time recognition technology and deep learning techniques, incorporating lane-level information and feature interactions. The modeling framework consists of three stages: real-time video data extraction and processing, the development of a Deep and Cross Network (DCN)-based real-time traffic conflict prediction model, and conflict-driven factor interpretability analysis through SHapley Additive exPlanations (SHAP). In the first stage, an efficient automated trajectory extraction system is designed to obtain vehicle trajectories in real time for dynamic traffic parameters and conflict frequency identification. In the second stage, a DCN model is developed to construct the relationships between dynamic traffic parameters, including their interactions, and traffic conflicts. In the third stage, SHAP explores the impact mechanisms of different dynamic traffic parameters on traffic conflicts. The model’s predictive performance and interpretability are evaluated using intersection video data from Changsha City, China. The results show that: 1) In real-time traffic conflict prediction at signalized intersections across different modified time-to-conflict thresholds (1.5s and 3.0s), the DCN model consistently outperformed statistical and machine learning models. 2) High traffic flow on main and secondary roads at signalized intersections significantly increases the complexity and frequency of conflicts, with varying sensitivity depending on the interaction of traffic flow, speed, and platoon length. 3) The proposed framework provides a safety measurement standard for data-driven road safety management methods.

Trajectories of Breastfeeding-Related Thoughts and Attitudes Among Low-Income Smoke-Exposed Pregnant Women: A Latent Class Growth Analysis.

Psychosocial predictors of breastfeeding and changes in those factors during pregnancy, along with the relationship of those changes with both breastfeeding and smoke use and exposure, are not well explored. The aim of this study was to identify distinct trajectories of psychosocial determinants of breastfeeding and smoking in pregnant women. We used a longitudinal study design and data from a randomized controlled trial conducted among smoke-exposed pregnant women and their infants. Participants were recruited early in pregnancy and were surveyed at ≤ 16 and 32 weeks gestation, delivery, 3 and 6 months postpartum for breastfeeding intentions, initiation, continuation, and smoke use and exposure. Psychosocial variables associated with breastfeeding were measured at baseline and 32 weeks gestation using the Mitra index, a structured questionnaire that assesses barriers and facilitators of breastfeeding intentions. Latent class growth analysis was performed using Mitra scores to identify distinct subgroups of participants with different trajectories. Sociodemographic characteristics, breastfeeding, and tobacco smoke use and exposure were compared across classes. Three or four trajectories were identified for each of the six Mitra scores. Trajectories for all Mitra scores were associated with breastfeeding intention and initiation. Overall, Mitra, knowledge, self-efficacy, social support, and time barrier classes all differed by tobacco smoke use or exposure. Trajectories of breastfeeding knowledge, self-efficacy, social support, and time to breastfeed/social barriers are associated with tobacco smoke use and exposure during pregnancy. Encouragement to breastfeed and to cease and avoid tobacco smoke should start early in pregnancy, focusing on these determinants to improve health outcomes.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Exploring the Nicotine-Parkinson's disease link – Insights from the UK Biobank

IntroductionEvidence suggests an inverse correlation between smoking and Parkinson's disease (PD), yet the mechanisms remain unclear. This study examines the changing risk relationship between smoking and PD diagnosis using alcohol consumption, another reward-driven behavior, as a comparative measure. MethodsA nested case-control study was conducted using the UK Biobank (UKBB) database. Participants in the prediagnostic phase of PD were identified, and self-reported data on tobacco and alcohol use were analyzed employing conditional binary logistic regression. Polynomial and piece-wise regression models were employed to discern shifting associations with PD over time. ResultsOf 502,304 participants (63 ± 5.3 years, 63 % male), 3049 prediagnostic PD cases were identified. Non-smokers had a heightened PD risk, and this association strengthened closer to diagnosis. The odds ratio (OR [95 % CI]) associated with PD in non-smokers was 2.02 [1.07–3.81] 1–4 years before diagnosis, compared to 1.36 [1.02–1.83] at >10-year intervals (linear trend, p = 0.012). The time trajectory of ORs was best depicted by a quadratic function, identifying a shift in risk 7.5 years before diagnosis documentation. Similar patterns emerged among alcohol non-consumers, with an 8.5-year interval inflection point. ConclusionThis study identified two disparate risk trajectories among non-smokers: an initial low-amplitude increased risk at prolonged prediagnostic intervals possibly related to genetic/personality factors, followed by a sharp escalation in risk association commencing 7–8 years before diagnosis, possibly propelled by reverse causality. Similar trends in alcohol consumption reinforce these conclusions. These findings could suggest that smoking cessation may serve as an early indicator of PD.

A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

Robots are increasingly deployed for search-and-rescue (SaR), in order to speed up rescuing the victims in the aftermath of disasters. These robots require effective mission planning approaches to determine time and space-efficient trajectories that steer them faster towards (moving) victims, while dealing with uncertainties. Model predictive control (MPC) is an effective optimization-based control approach that has been used to steer robots along reference trajectories determined by higher level controllers. Determining the trajectory of the robots directly via MPC has the advantage of optimizing multiple SaR criteria while handling the constraints. We, thus, introduce a path planning approach based on MPC for indoor SaR robots that allows the robot to systematically chase the moving victims, when no reference trajectory is provided. The proposed approach combines target-oriented and coverage-oriented search, and allows for systematic handling of environmental uncertainties, by deploying a robust tube-based version of the introduced MPC formulation. In addition, we model the movements of the victims for MPC, by adopting an existing evacuation model. We present a case study, using Gazebo, MATLAB, and ROS, where the performance of the proposed MPC controller is evaluated compared to four state-of-the-art methods (two target-oriented methods based on MPC and A* and two heuristic algorithms for area coverage). The results show that, while robust to uncertainties, our approach overall outperforms the other methods, with regards to victim detection, area coverage, and mission time.

Mechanisms of soil organic carbon and nitrogen stabilization in mineral-associated organic matter – insights from modeling in phase space

Abstract. Understanding the mechanisms of plant-derived carbon (C) and nitrogen (N) transformation and stabilization in soil is fundamental for predicting soil capacity to mitigate climate change and support other soil functions. The decomposition of plant residues and particulate organic matter (POM) contributes to the formation of mineral-associated (on average more stable) organic matter (MAOM) in soil. MAOM is formed from the binding of dissolved organic matter (ex vivo pathway) or microbial necromass and bioproducts (in vivo pathway) to minerals and metal colloids. Which of these two soil organic matter (SOM) stabilization pathways is more important and under which conditions remains an open question. To address this question, we propose a novel diagnostic model to describe C and N dynamics in MAOM as a function of the dynamics of residues and POM decomposition. Focusing on relations among soil compartments (i.e., modeling in phase space) rather than time trajectories allows isolating the fundamental processes underlying stabilization. Using this diagnostic model in combination with a database of 36 studies in which residue C and N were tracked into POM and MAOM, we found that MAOM is predominantly fueled by necromass produced by microbes decomposing residues and POM. The relevance of this in vivo pathway is higher in clayey soils but lower in C-rich soils and with N-poor added residues. Overall, our novel modeling in phase space proved to be a sound diagnostic tool for the mechanistic investigation of soil C dynamics and supported the current understanding of the critical role of both microbial transformation and mineral capacity for the stabilization of C in mineral soils.

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

Monitoring reaction time to digital device in the very-old to detect early cognitive decline

Early detection of cognitive decline is essential for timely intervention and effective management of age-related impairments. We monitored repetitive reaction times to a simple task on senior-friendly tablet computers among 72 functionally independent older adults, with a mean age of 82, ranging up to 100 years, within natural settings over two years. Functional principal component analyses revealed a consistent decrease in reaction time in line with their task experience among those without subjective cognitive decline. Conversely, individuals reporting subjective cognitive decline showed no consistent trend and exhibited wide variability over time. These distinctive reaction time trajectories in very old adults suggest the potential for monitoring as a non-invasive, convenient method for early detection of cognitive impairment.

Wake-up of sleeping beauty patent families: The global non-equilibrium diffusion of technological knowledge

Transformative technologies are the key to leading future technology innovation and economic development. However, with the exchange of technological knowledge among countries and the explosive growth of patented technology, the uneven flow and application of technology across geographic regions has become increasingly apparent, making it more and more difficult to track and absorb potentially transformative technology (PTT). As a proxy, the wake-up trajectories of the sleeping beauty patent family (SBPF) reflect the unbalanced distribution and application of PTT but still lack in-depth discussion. Therefore, this study adopted the parameter-free criteria to identify SBPFs and revealed the globalized diffusion patterns of PTT behind SBPFs' wake-up time and pace trajectories by taking “Polymerase Chain Reaction” technology as an example. The findings show that SBPFs’ wake-up has five-time and four-spatial trajectories and presents a small-scale diffusion in local areas. Meanwhile, PTT shows five diffusion patterns, achieving an unbalanced and centralized diffusion worldwide. These provide theoretical support for predicting the global development of PTT, and practical guidance for choosing technical direction, grasping market opportunities, and optimizing the national innovation environment.

Resonance and attraction domain analysis of asymmetric duffing systems with fractional damping in two degrees of freedom

This article establishes a two degree of freedom asymmetric Duffing system model with fractional damping through the study of the Jeffcott rotor system with horizontal support, and analyzes the dynamic behavior of its resonance and attraction domain structures. The second-order approximate solution of the system equation is obtained using a multiscale method, and the slow flow modulation equations of both the amplitudes and phases are extracted. The comparison between approximate and numerical solutions has shown their high consistency and the Routh Hurwitz criterion is utilized to study the stability of the system. Finally, we explore the effect of fractional damping on the vibration behavior of the system using amplitude frequency curves, attractive watersheds, and time trajectory plots, including both negative and positive values of the nonlinear stiffness coefficient. The analysis shows that if the coefficient and order of the fractional damping term are reasonably selected, the horizontal and vertical displacements of the system vibration can be effectively controlled. The importance of this work lies in its application in rotor vibration, which has significant implications for the study of the system under investigation.

Fault tolerant motion planning for a quadrotor subject to complete rotor failure

Assuring safety of a quadrotor subject to rotor failure has been heavily investigated at the control level in view of fault tolerant control (FTC) approach. Yet, the existing FTCs are often concerned with tracking the reference motion even when that reference may not be safely trackable due to the physical constraints of the quadrotor. This paper tackles the faulty quadrotor safety at the planner level, proposing a fault tolerant motion planner. Starting from the formal backward reachability problem formulation, the proposed motion planner generates the time trajectory of the coupled rotational and translational motions that safely guide the faulty quadrotor. The generated trajectory is theoretically guaranteed to be tracked by the embedded FTC without violating the physical constraints. Further, the trajectory is prescribed as an analytical closed-form expression and thus suitable for real-time emergency maneuvers. The effectiveness of the proposed motion planner is numerically validated in conjunction with the different FTC techniques and compared to the existing planning method. The simulation results clearly signify that the proposed planner can successfully complement the fault tolerance of quadrotor. The supplements including code implementations are available on GitHub repository: https://github.com/HMCL-UNIST/Fault-tolerant-motion-planner.

Recovering biomolecular network dynamics from single-cell omics data requires three time points

Single-cell omics technologies can measure millions of cells for up to thousands of biomolecular features, enabling data-driven studies of complex biological networks. However, these high-throughput experimental techniques often cannot track individual cells over time, thus complicating the understanding of dynamics such as time trajectories of cell states. These “dynamical phenotypes” are key to understanding biological phenomena such as differentiation fates. We show by mathematical analysis that, in spite of high dimensionality and lack of individual cell traces, three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics. Moreover, we show through numerical simulations that an interaction matrix can be accurately determined with three or more time-points even in the presence of sampling and measurement noise typical of single-cell omics. Our results can guide the design of single-cell omics time-course experiments, and provide a tool for data-driven phase-space analysis.

Intrinsic sense of touch for intuitive physical human-robot interaction.

The sense of touch is a property that allows humans to interact delicately with their physical environment. This article reports on a technological advancement in intuitive human-robot interaction that enables an intrinsic robotic sense of touch without the use of artificial skin or tactile instrumentation. On the basis of high-resolution joint-force-torque sensing in a redundant arrangement, we were able to let the robot sensitively feel the surrounding environment and accurately localize touch trajectories in space and time that were applied on its surface by a human. Through an intertwined combination of manifold learning techniques and artificial neural networks, the robot identified and interpreted those touch trajectories as machine-readable letters, symbols, or numbers. This opens up unexplored opportunities in terms of intuitive and flexible interaction between human and robot. Furthermore, we showed that our concept of so-called virtual buttons can be used to straightforwardly implement a tactile communication link, including switches and slider bars, which are complementary to speech, hardware buttons, and control panels. These interaction elements could be freely placed, moved, and configured in arbitrary locations on the robot structure. The intrinsic sense of touch we proposed in this work can serve as the basis for an advanced category of physical human-robot interaction that has not been possible yet, enabling a shift from conventional modalities toward adaptability, flexibility, and intuitive handling.

Finite Difference Model of Substrate Channeling in TCA Cycle Enzymes

The Tricarboxylic Acid (TCA) cycle is a highly optimized metabolic pathway. Of particular interest in the TCA cycle is the malate dehydrogenase—citrate synthase (MDH-CS) complex, which accomplishes citrate production from malate with an oxaloacetate intermediate. This complex was studied experimentally by Bulutoglu et al, who showed that mutation of positively-charged residues on the complex surface effected the dynamic response of citrate production as measured by lag time experiments.1 More recently, we have created a Markov State model that was used to predict the transfer efficiency of each complex and determine the reaction pathways on the surface.2 Utilizing the kinetic parameters of both the recombinant and mutant complexes determined experimentally1and Markov state transition matrix produced by previous modeling2, we report a finite difference model to calculate time trajectories of the surface and bulk occupancies by OAA. Solution of a system of differential mass balance equations, based on the transient matrix, provide the occupancy probability at each node in the network. The lag time of MDH-CS was determined computationally to be comparable to experiment for both the recombinant and mutant complex with lag times of 0.44 and 2.1 seconds, respectively.1 Using the model, the dynamics of each of the four possible reaction pathways between the the two source (MDH) active sites and the two sink (CS) sites could be studied independently. This analysis provides a dynamic model for intermediate transport in an electrostatically channeled system, and can be used as a predictive tool to provide mechanistic insight into pathway dominance. We gratefully acknowledge support from the Army Research Office MURI (#W911NF1410263) via The University of Utah. References B. Bulutoglu, K. E. Garcia, F. Wu, S. D. Minteer, and S. Banta, ACS Chemical Biology, 11, 2847–53 (2016). doi:10.1021/acschembio.6b00523Y. Xie, S. D. Minteer, S. Banta, and S. Calabrese Barton, ACS Nanoscience Au, 2, 414-421 (2022). doi:10.1021/acsnanoscienceau.2c00011 Figure 1