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1. Modeling material transport regulation and traffic jam in neurons using PDE-constrained optimization

   https://doi.org/10.1038/s41598-022-07861-6  

   Li, Angran ; Zhang, Yongjie Jessica ( December 2022 , Scientific Reports)

   Abstract The intracellular transport process plays an important role in delivering essential materials throughout branched geometries of neurons for their survival and function. Many neurodegenerative diseases have been associated with the disruption of transport. Therefore, it is essential to study how neurons control the transport process to localize materials to necessary locations. Here, we develop a novel optimization model to simulate the traffic regulation mechanism of material transport in complex geometries of neurons. The transport is controlled to avoid traffic jam of materials by minimizing a pre-defined objective function. The optimization subjects to a set of partial differential equation (PDE) constraints that describe the material transport process based on a macroscopic molecular-motor-assisted transport model of intracellular particles. The proposed PDE-constrained optimization model is solved in complex tree structures by using isogeometric analysis (IGA). Different simulation parameters are used to introduce traffic jams and study how neurons handle the transport issue. Specifically, we successfully model and explain the traffic jam caused by reduced number of microtubules (MTs) and MT swirls. In summary, our model effectively simulates the material transport process in healthy neurons and also explains the formation of a traffic jam in abnormal neurons. Our results demonstrate that both geometry and MT structure play important roles in achieving an optimal transport process in neuron. 

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2. Deep learning of material transport in complex neurite networks

   https://doi.org/10.1038/s41598-021-90724-3  

   Li, Angran ; Barati Farimani, Amir ; Zhang, Yongjie Jessica ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and $$120 \sim 330$$ 120 ∼ 330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks. 

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3. Deep Learning of Material Transport in Complex Neurite Networks

   Li, A ; Farimani, A.B. ; Zhang, Y.J. ( January 2021 , Scientific reports)

   Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and 120∼330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks. 

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4. An Isogeometric Analysis Computational Platform for Material Transport Simulations in Complex Neurite Networks

   https://doi.org/10.32604/mcb.2019.06479  

   Angran Li, Xiaoqi Chai ( January 2019 , Molecular & cellular biomechanics)

   Neurons exhibit remarkably complex geometry in their neurite networks. So far, how materials are transported in the complex geometry for survival and function of neurons remains an unanswered question. Answering this question is fundamental to understanding the physiology and disease of neurons. Here, we have developed an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We modeled the transport process by reaction-diffusion-transport equations and represented geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solved the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solved the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Using our IGA solver, we simulated material transport in three basic models of the network geometry: a single neurite, a neurite bifurcation, and a neurite tree with three bifurcations. In addition, the robustness of our solver is illustrated by simulating material transport in three representative and complex neurite networks. From the simulation we discovered several spatial patterns of the transport process. Together, our simulation provides key insights into how material transport in neurite networks is mediated by their complex geometry. 

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5. Porosity problems: Comparing and reviewing methods for estimating porosity and volume of wood jams in the field

   https://doi.org/10.1002/esp.4969  

   Livers, Bridget ; Lininger, Katherine B. ; Kramer, Natalie ; Sendrowski, Alicia ( August 2020 , Earth Surface Processes and Landforms)

   Porosity, or void space, of large wood jams in stream systems has implications for estimating wood volumes and carbon storage, the impacts of jams on geomorphic and ecological processes, and instream habitat. Estimating porosity and jam dimensions (i.e. jam volume) in the field is a common method of measuring wood volume in jams. However, very few studies explicitly address the porosity values in jams, how porosity is calculated and assessed for accuracy, and the effect such estimates have on carbon and wood budgets in river corridors. We compare methods to estimate jam porosity and wood volume using field data from four different depositional environments in North America (jam types include small in‐channel jams, large channel‐margin jams, a large island apex jam, and a large coastal jam), and compare the results with previous studies. We find that visual estimates remain the most time‐efficient method for porosity estimation in the field, although they appear to underpredict back‐calculated porosity values; the accuracy of jam porosity, and thus wood volume, estimates are difficult to definitively measure. We also find that porosity appears to be scale invariant, dictated mostly by jam type, (which is influenced by depositional processes), rather than the size of the jam. Wood piece sorting and structural organization are likely the most influential properties on jam porosity, and these factors vary according to depositional environment. We provide a framework and conceptual model that uses these factors to demonstrate how modeled jam porosity values differ and give recommendations as a catalyst for future work on porosity of wood jams. We conclude that jam type and size and/or the study goals may dictate which porosity method is the most appropriate, and we call for greater transparency and reporting of porosity methods in future studies. © 2020 John Wiley & Sons, Ltd.

    

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