Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Li, Shiwei; Quan, Tingwei; Cheng, Xu; Huang, Qing; Kang, Huifang; Chen, Yijun; Li, Anan; Fu, Ling; Luo, Qingming; Gong, Hui; Zeng, Shaoqun

doi:10.3389/fnana.2019.00018

Cited by 18 publications

(13 citation statements)

References 49 publications

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“…Additionally, we used the curvature of neurites as a measure of neurite turning to further confirm the orientation of neurons derived from NSCs. When the morphology of neurites is tortuous, the curvature of the skeleton points is usually large, so smaller curvature represents more oriented neurites . The representative curvatures of neurites extend from NSCs cultured on TCPS, wing, and rGO/BDNF/GelMA-integrated wing in Figure e, thus further confirming the orientation of neurites along the wing’s nanoridges.…”

Section: Results and Discussionmentioning

confidence: 52%

Conductive Nerve Guidance Conduits Based on Morpho Butterfly Wings for Peripheral Nerve Repair

Wang

Guo

et al. 2022

ACS Nano

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Peripheral nerve injury (PNI), causing loss of sensory and motor function, is a complex and challenging disease in the clinic due to the restricted regeneration capacity. Nerve guidance conduits (NGCs) have become a promising substitute for peripheral nerve regeneration, but their efficacy is often limited. Here, inspired by the physiological structures of peripheral nerves, we present a conductive topological scaffold for nerve repair by modifying Morpho butterfly wing with reduced graphene oxide (rGO) nanosheets and methacrylated gelatin (GelMA) hydrogel encapsulated brain-derived neurotrophic factor (BDNF). Benefiting from the biocompatibility of GelMA hydrogel, the conductivity of rGO and parallel nanoridge structures of wing scales, PC12 cells, and neural stem cells grown on the modified wing have an increased neurite length with guided cellular orientation. In addition, the NGCs are successfully obtained by manually rolling up the scaffolds and exhibited great performance in repairing 10 mm sciatic nerve defects in rats, and we believe that the NGCs can be applied in reparing longer nerve defects in the future by further optimization. We also demonstrate the feasibility of electrically conductive NGCs based on the rGO/BDNF/GelMA-integrated Morpho butterfly wing as functional nerve regeneration conduits, which may have potential value for application in repairing peripheral nerve injuries.

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Section: Results and Discussionmentioning

confidence: 52%

Conductive Nerve Guidance Conduits Based on Morpho Butterfly Wings for Peripheral Nerve Repair

Wang

Guo

et al. 2022

ACS Nano

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“…Degeneracy is not a problem for the 1 − minimization which should be in or near the range ofỹ, it does not depend on i = D T i R T RD i , it's not singular in classical discriminant analysis. The stable version 1 − minimization (7) or (12) is called Lasso [10] in statistical literature. When the solution is sparse, it effectively standardizes the highly underdetermined linear regression.…”

Section: A Function Of Feature Extractionmentioning

confidence: 99%

“…and the researches maily focus on the following aspects: when the basic elements or signal are sparse enough, the sparse representation can be effectively calculated by convex optimization [6], although commonly it may be very difficult. In order to solve such problems, the coefficients in linear combination are dealt with in paper [6] and [10], instead of solving the problem of number of non-zero coefficients(i.e., 0 − norm). The initial purpose of sparse representation algorithm is not to identify or classify, but to make the representation and compression of signal have a lower sampling rate than Shannon-Nyquist.…”

Section: Introductionmentioning

confidence: 99%

Identification of Underwater Targets Based on Sparse Representation

2020

IEEE Access

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We consider using sparse representations to identify underwater targets, since underwater acoustic signal have sparse characteristics. We consider the identification problem as one of the identifying among multiple linear regression models and believe that the new theory from sparse signal representation provides the key to solving this problem. Based on a sparse representation computed by 1 − minimization, we propose a general classification algorithm for (hydroacoustic signal-based) targets identification. This new framework provides new insights into identifying two key issues in underwater targets: feature extraction and robustness of signal loss and noise interference. For feature extraction, we point out that feature extraction is no longer critical if the sparseness of the underwater acoustic signal is properly utilized. The critical is whether the number of features is large enough and whether the sparse representation is correctly computed. This framework can handle errors due to signal loss and noise interference uniformly by exploiting the fact that these errors are often sparse with respect to the standard (hydroacoustic signal) basis. Extensive experiments have been conducted based on a public underwater acoustic signal sampling set to verify the efficacy of the proposed algorithm and corroborate the above claims. INDEX TERMS Identification of underwater targets, sparse representation, 1 − norm, compressed sensing.

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“…The curvature of the specific intermediate point has a nonzero value and corresponds to the tortuous structure. When neuronal morphology includes the tortuous structures, the curvature of the skeleton points is large . Therefore, smaller values of curvature indicate more aligned morphology of the neurites.…”

mentioning

confidence: 99%

“…When neuronal morphology includes the tortuous structures, the curvature of the skeleton points is large. 40 Therefore, smaller values of curvature indicate more aligned morphology of the neurites. Ten representative neurite curvatures were presented in Figure 3C.…”

mentioning

confidence: 99%

3D Free-Standing Ordered Graphene Network Geometrically Regulates Neuronal Growth and Network Formation

et al. 2020

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The control of cell−microenvironment interactions plays a pivotal role in constructing specific scaffolds for tissue engineering. Here, we fabricated a 3D free-standing ordered graphene (3D-OG) network with a precisely defined pattern. When primary cortical cells are cultured on 3D-OG scaffolds, they form well-defined 3D connections. Astrocytes have a more ramified shape similar to that seen in vivo because of the nanosized ripples and wrinkles on the surface of graphene skeleton. Neurons have axons and dendrites aligned along the graphene skeleton, allowing the formation of neuronal networks with highly controlled connections. Neuronal networks have higher electrical activity with functional signaling over a long distance along the graphene skeleton. Our study, for the first time, investigated the geometrical cues on ordered neuronal growth and network formation with the support of graphene in 3D, which therefore advanced the development of customized scaffolds for brain−machine interfaces or neuroprosthetic devices.

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Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Cited by 18 publications

References 49 publications

Conductive Nerve Guidance Conduits Based on Morpho Butterfly Wings for Peripheral Nerve Repair

Conductive Nerve Guidance Conduits Based on Morpho Butterfly Wings for Peripheral Nerve Repair

Identification of Underwater Targets Based on Sparse Representation

3D Free-Standing Ordered Graphene Network Geometrically Regulates Neuronal Growth and Network Formation

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