1
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Dillon AP, Moslehi S, Brouse B, Keremane S, Philliber S, Griffiths W, Rowland C, Smith JH, Taylor RP. Evolution of Retinal Neuron Fractality When Interfacing with Carbon Nanotube Electrodes. Bioengineering (Basel) 2024; 11:823. [PMID: 39199781 PMCID: PMC11351692 DOI: 10.3390/bioengineering11080823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 08/06/2024] [Accepted: 08/06/2024] [Indexed: 09/01/2024] Open
Abstract
Exploring how neurons in the mammalian body interact with the artificial interface of implants can be used to learn about fundamental cell behavior and to refine medical applications. For fundamental and applied research, it is crucial to determine the conditions that encourage neurons to maintain their natural behavior during interactions with non-natural interfaces. Our previous investigations quantified the deterioration of neuronal connectivity when their dendrites deviate from their natural fractal geometry. Fractal resonance proposes that neurons will exhibit enhanced connectivity if an implant's electrode geometry is matched to the fractal geometry of the neurons. Here, we use in vitro imaging to quantify the fractal geometry of mouse retinal neurons and show that they change during interaction with the electrode. Our results demonstrate that it is crucial to understand these changes in the fractal properties of neurons for fractal resonance to be effective in the in vivo mammalian system.
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Affiliation(s)
- Aiden P. Dillon
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Saba Moslehi
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Bret Brouse
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Saumya Keremane
- Department of Biology, University of Oregon, Eugene, OR 97403, USA
- Department of Biology, Institute of Neurobiology, University of Oregon, Eugene, OR 97403, USA
| | - Sam Philliber
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Willem Griffiths
- Department of Biology, University of Oregon, Eugene, OR 97403, USA
| | - Conor Rowland
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Julian H. Smith
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
| | - Richard P. Taylor
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
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2
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Ferreira J, Michiels J, Herregraven M, Korevaar PA. Myelin Surfactant Assemblies as Dynamic Pathways Guiding the Growth of Electrodeposited Copper Dendrites. J Am Chem Soc 2024; 146:19205-19217. [PMID: 38959136 PMCID: PMC11258786 DOI: 10.1021/jacs.4c04346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/05/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
Self-organization of inorganic matter enables bottom-up construction of materials with target shapes suited to their function. Positioning the building blocks in the growth process involves a well-balanced interplay of the reaction and diffusion. Whereas (supra)molecular structures have been used to template such growth processes, we reasoned that molecular assemblies can be employed to actively create concentration gradients that guide the deposition of solid, wire-like structures. The core of our approach comprises the interaction between myelin assemblies that deliver copper(II) ions to the tips of copper dendrites, which in turn grow along the Cu2+ gradient upon electrodeposition. First, we successfully include Cu2+ ions among amphiphile bilayers in myelin filaments, which grow from tri(ethylene glycol) monododecyl ether (C12E3) source droplets over air-water interfaces. Second, we characterize the growth of dendritic copper structures upon electrodeposition from a negative electrode at the sub-mM Cu2+ concentrations that are anticipated upon release from copper(II)-loaded myelins. Third, we assess the intricate growth of copper dendrites upon electrodeposition, when combined with copper(II)-loaded myelins. The myelins deliver Cu2+ at a negative electrode, feeding copper dendrite growth upon electrodeposition. Intriguingly, the copper dendrites follow the Cu2+ gradient toward the myelins and grow along them toward the source droplet. We demonstrate the growth of dynamic connections among electrodes and surfactant droplets in reconfigurable setups─featuring a unique interplay between molecular assemblies and inorganic, solid structures.
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Affiliation(s)
- José Ferreira
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, Nijmegen 6525 AJ, The Netherlands
| | - Jeroen Michiels
- TechnoCentre,
Faculty of Science, Radboud University, Heyendaalseweg 135, Nijmegen 6525 AJ, The Netherlands
| | - Marty Herregraven
- TechnoCentre,
Faculty of Science, Radboud University, Heyendaalseweg 135, Nijmegen 6525 AJ, The Netherlands
| | - Peter A. Korevaar
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, Nijmegen 6525 AJ, The Netherlands
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3
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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4
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. eLife 2024; 12:RP90597. [PMID: 38345923 PMCID: PMC10942544 DOI: 10.7554/elife.90597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024] Open
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W Wheeler
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Jeffrey D Kopsick
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Alexander O Komendantov
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
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5
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.12.540597. [PMID: 37425693 PMCID: PMC10327012 DOI: 10.1101/2023.05.12.540597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W. Wheeler
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Jeffrey D. Kopsick
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Alexander O. Komendantov
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
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6
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Moslehi S, Rowland C, Smith JH, Watterson WJ, Griffiths W, Montgomery RD, Philliber S, Marlow CA, Perez MT, Taylor RP. Fractal Electronics for Stimulating and Sensing Neural Networks: Enhanced Electrical, Optical, and Cell Interaction Properties. ADVANCES IN NEUROBIOLOGY 2024; 36:849-875. [PMID: 38468067 DOI: 10.1007/978-3-031-47606-8_43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Imagine a world in which damaged parts of the body - an arm, an eye, and ultimately a region of the brain - can be replaced by artificial implants capable of restoring or even enhancing human performance. The associated improvements in the quality of human life would revolutionize the medical world and produce sweeping changes across society. In this chapter, we discuss several approaches to the fabrication of fractal electronics designed to interface with neural networks. We consider two fundamental functions - stimulating electrical signals in the neural networks and sensing the location of the signals as they pass through the network. Using experiments and simulations, we discuss the favorable electrical performances that arise from adopting fractal rather than traditional Euclidean architectures. We also demonstrate how the fractal architecture induces favorable physical interactions with the cells they interact with, including the ability to direct the growth of neurons and glia to specific regions of the neural-electronic interface.
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Affiliation(s)
- S Moslehi
- Physics Department, University of Oregon, Eugene, OR, USA
| | - C Rowland
- Physics Department, University of Oregon, Eugene, OR, USA
| | - J H Smith
- Physics Department, University of Oregon, Eugene, OR, USA
| | - W J Watterson
- Physics Department, University of Oregon, Eugene, OR, USA
| | - W Griffiths
- Physics Department, University of Oregon, Eugene, OR, USA
| | - R D Montgomery
- Physics Department, University of Oregon, Eugene, OR, USA
| | - S Philliber
- Physics Department, University of Oregon, Eugene, OR, USA
| | - C A Marlow
- Physics Department, California Polytechnic State University, San Luis Obispo, CA, USA
| | - M-T Perez
- Department of Clinical Sciences Lund, Division of Ophthalmology, Lund University, Lund, Sweden
| | - R P Taylor
- Physics Department, University of Oregon, Eugene, OR, USA.
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7
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Rowland C, Moslehi S, Smith JH, Harland B, Dalrymple-Alford J, Taylor RP. Fractal Resonance: Can Fractal Geometry Be Used to Optimize the Connectivity of Neurons to Artificial Implants? ADVANCES IN NEUROBIOLOGY 2024; 36:877-906. [PMID: 38468068 DOI: 10.1007/978-3-031-47606-8_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
In parallel to medical applications, exploring how neurons interact with the artificial interface of implants in the human body can be used to learn about their fundamental behavior. For both fundamental and applied research, it is important to determine the conditions that encourage neurons to maintain their natural behavior during these interactions. Whereas previous biocompatibility studies have focused on the material properties of the neuron-implant interface, here we discuss the concept of fractal resonance - the possibility that favorable connectivity properties might emerge by matching the fractal geometry of the implant surface to that of the neurons.To investigate fractal resonance, we first determine the degree to which neurons are fractal and the impact of this fractality on their functionality. By analyzing three-dimensional images of rat hippocampal neurons, we find that the way their dendrites fork and weave through space is important for generating their fractal-like behavior. By modeling variations in neuron connectivity along with the associated energetic and material costs, we highlight how the neurons' fractal dimension optimizes these constraints. To simulate neuron interactions with implant interfaces, we distort the neuron models away from their natural form by modifying the dendrites' fork and weaving patterns. We find that small deviations can induce large changes in fractal dimension, causing the balance between connectivity and cost to deteriorate rapidly. We propose that implant surfaces should be patterned to match the fractal dimension of the neurons, allowing them to maintain their natural functionality as they interact with the implant.
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Affiliation(s)
- C Rowland
- Physics Department, University of Oregon, Eugene, OR, USA
| | - S Moslehi
- Physics Department, University of Oregon, Eugene, OR, USA
| | - J H Smith
- Physics Department, University of Oregon, Eugene, OR, USA
| | - B Harland
- School of Pharmacy, University of Auckland, Auckland, New Zealand
| | - J Dalrymple-Alford
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - R P Taylor
- Physics Department, University of Oregon, Eugene, OR, USA.
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8
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Karperien AL, Jelinek HF. Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions. ADVANCES IN NEUROBIOLOGY 2024; 36:149-172. [PMID: 38468031 DOI: 10.1007/978-3-031-47606-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.
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Affiliation(s)
- Audrey L Karperien
- School of Community Health, Charles Sturt University, Albury, NSW, Australia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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9
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Porcaro C, Moaveninejad S, D'Onofrio V, DiIeva A. Fractal Time Series: Background, Estimation Methods, and Performances. ADVANCES IN NEUROBIOLOGY 2024; 36:95-137. [PMID: 38468029 DOI: 10.1007/978-3-031-47606-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience (DNS) and Padova Neuroscience Center (PNC), University of Padova, Padua, Italy.
- Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR), Rome, Italy.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
| | | | | | - Antonio DiIeva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
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10
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Vlasko-Vlasov VK, Divan R, Rosenmann D, Welp U, Glatz A, Kwok WK. Multiquanta flux jumps in superconducting fractal. Sci Rep 2023; 13:12601. [PMID: 37537249 PMCID: PMC10400563 DOI: 10.1038/s41598-023-39733-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/30/2023] [Indexed: 08/05/2023] Open
Abstract
We study the magnetic field response of millimeter scale fractal Sierpinski gaskets (SG) assembled of superconducting equilateral triangular patches. Directly imaged quantitative induction maps reveal hierarchical periodic filling of enclosed void areas with multiquanta magnetic flux, which jumps inside the voids in repeating bundles of individual flux quanta Φ0. The number Ns of entering flux quanta in different triangular voids of the SG is proportional to the linear size s of the void, while the field periodicity of flux jumps varies as 1/s. We explain this behavior by modeling the triangular voids in the SG with effective superconducting rings and by calculating their response following the London analysis of persistent currents, Js, induced by the applied field Ha and by the entering flux. With changing Ha, Js reaches a critical value in the vertex joints that connect the triangular superconducting patches and allows the giant flux jumps into the SG voids through phase slips or multiple Abrikosov vortex transfer across the vertices. The unique flux behavior in superconducting SG patterns, may be used to design tunable low-loss resonators with multi-line high-frequency spectrum for microwave technologies.
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Affiliation(s)
| | - Ralu Divan
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Daniel Rosenmann
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Ulrich Welp
- Materials Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Andreas Glatz
- Materials Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
- Department of Physics, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Wai-Kwong Kwok
- Materials Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
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11
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Srinivasan A, Srinivasan A, Goodman MR, Riceberg JS, Guise KG, Shapiro ML. Hippocampal and Medial Prefrontal Cortex Fractal Spiking Patterns Encode Episodes and Rules. CHAOS, SOLITONS, AND FRACTALS 2023; 171:113508. [PMID: 37251275 PMCID: PMC10217776 DOI: 10.1016/j.chaos.2023.113508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
A central question in neuroscience is how the brain represents and processes information to guide behavior. The principles that organize brain computations are not fully known, and could include scale-free, or fractal patterns of neuronal activity. Scale-free brain activity may be a natural consequence of the relatively small subsets of neuronal populations that respond to task features, i.e., sparse coding. The size of the active subsets constrains the possible sequences of inter-spike intervals (ISI), and selecting from this limited set may produce firing patterns across wide-ranging timescales that form fractal spiking patterns. To investigate the extent to which fractal spiking patterns corresponded with task features, we analyzed ISIs in simultaneously recorded populations of CA1 and medial prefrontal cortical (mPFC) neurons in rats performing a spatial memory task that required both structures. CA1 and mPFC ISI sequences formed fractal patterns that predicted memory performance. CA1 pattern duration, but not length or content, varied with learning speed and memory performance whereas mPFC patterns did not. The most common CA1 and mPFC patterns corresponded with each region's cognitive function: CA1 patterns encoded behavioral episodes which linked the start, choice, and goal of paths through the maze whereas mPFC patterns encoded behavioral "rules" which guided goal selection. mPFC patterns predicted changing CA1 spike patterns only as animals learned new rules. Together, the results suggest that CA1 and mPFC population activity may predict choice outcomes by using fractal ISI patterns to compute task features.
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Affiliation(s)
- Aditya Srinivasan
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, 47 New Scotland Ave, Mail Code 126, Albany, NY 12208
| | - Arvind Srinivasan
- College of Health Sciences, California Northstate University, 2910 Prospect Park Drive, Rancho Cordova, CA 95670
| | - Michael R. Goodman
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, 47 New Scotland Ave, Mail Code 126, Albany, NY 12208
| | - Justin S. Riceberg
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, 47 New Scotland Ave, Mail Code 126, Albany, NY 12208
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, 1470 Madison Avenue New York, NY 10029
| | - Kevin G. Guise
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, 1470 Madison Avenue New York, NY 10029
| | - Matthew L. Shapiro
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, 47 New Scotland Ave, Mail Code 126, Albany, NY 12208
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12
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McIntosh J, Mekrouda I, Dashti M, Giuraniuc CV, Banks RW, Miles GB, Bewick GS. Development of abnormalities at the neuromuscular junction in the SOD1-G93A mouse model of ALS: dysfunction then disruption of postsynaptic structure precede overt motor symptoms. Front Mol Neurosci 2023; 16:1169075. [PMID: 37273905 PMCID: PMC10237339 DOI: 10.3389/fnmol.2023.1169075] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/12/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction The ultimate deficit in amyotrophic lateral sclerosis (ALS) is neuromuscular junction (NMJ) loss, producing permanent paralysis, ultimately in respiratory muscles. However, understanding the functional and structural deficits at NMJs prior to this loss is crucial for therapeutic strategy design. Should early interventions focus on reversing denervation, or supporting largely intact NMJs that are functionally impaired? We therefore determined when functional and structural deficits appeared in diaphragmatic NMJs relative to the onset of hindlimb tremor (the first overt motor symptoms) in vivo in the SOD1-G93A mouse model of ALS. Materials and methods We employed electrophysiological recording of NMJ postsynaptic potentials for spontaneous and nerve stimulation-evoked responses. This was correlated with fluorescent imaging microscopy of the postsynaptic acetylcholine receptor (AChR) distribution throughout the postnatal developmental timecourse from 2 weeks to early symptomatic ages. Results Significant reduction in the amplitudes of spontaneous miniature endplate potentials (mEPPs) and evoked EPPs emerged only at early symptomatic ages (in our colony, 18-22 weeks). Reductions in mEPP frequency, number of vesicles per EPP, and EPP rise time were seen earlier, at 16weeks, but this reversed by early symptomatic ages. However, the earliest and most striking impairment was an inability to maintain EPP amplitude during a 20 Hz stimulus train, which appeared 6 weeks before overt in vivo motor symptoms. Despite this, fluorescent α-bungarotoxin labelling revealed no systematic, progressive changes in 11 comprehensive NMJ morphological parameters (area, shape, compactness, number of acetylcholine receptor, AChR, regions, etc.) with disease progression. Rather, while NMJs were largely normally-shaped, from 16 weeks there was a progressive and substantial disruption in AChR concentration and distribution within the NMJ footprint. Discussion Thus, NMJ functional deficits appear at least 6 weeks before motor symptoms in vivo, while structural deficits occur 4 weeks later, and predominantly within NMJs. These data suggest initial therapies focused on rectifying suboptimal NMJ function could produce effective relief of symptoms of weakness.
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Affiliation(s)
- Jayne McIntosh
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Imane Mekrouda
- School of Biosciences, Cardiff University, Cardiff, United Kingdom
| | - Maryam Dashti
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | | | - Robert W. Banks
- Department of Biosciences, Durham University, Durham, United Kingdom
| | - Gareth B. Miles
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, United Kingdom
| | - Guy S. Bewick
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
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13
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Grosu GF, Hopp AV, Moca VV, Bârzan H, Ciuparu A, Ercsey-Ravasz M, Winkel M, Linde H, Mureșan RC. The fractal brain: scale-invariance in structure and dynamics. Cereb Cortex 2023; 33:4574-4605. [PMID: 36156074 PMCID: PMC10110456 DOI: 10.1093/cercor/bhac363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/12/2022] Open
Abstract
The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.
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Affiliation(s)
- George F Grosu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | | | - Vasile V Moca
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
| | - Harald Bârzan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Andrei Ciuparu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Maria Ercsey-Ravasz
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Physics, Babes-Bolyai University, Str. Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania
| | - Mathias Winkel
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Helmut Linde
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Raul C Mureșan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
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14
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Pushchin I, Kondrashev S, Borshcheva T. The structure and diversity of retinal ganglion cells in the masked greenling Hexagrammos octogrammus Pallas, 1814 (Pisces: Scorpaeniformes: Hexagrammidae). JOURNAL OF FISH BIOLOGY 2023; 102:550-563. [PMID: 36482763 DOI: 10.1111/jfb.15287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
The authors studied the structure and diversity of retinal ganglion cells (GC) in the masked greenling Hexagrammos octogrammus. In vivo labelling with horseradish peroxidase revealed GCs of various structures in retinal wholemounts. A total of 154 cells were camera lucida drawn, and their digital models were generated. Each cell was characterized by 17 structural and topological parameters. Using nine clustering algorithms, a variety of clusterings were obtained. The optimum clustering was found using silhouette analysis. It was based on a set of three variables associated with dendritic field size and dendrite stratification depth in the retina. A total of nine cell types were discovered. A number of non-parametric tests showed significant pair-wise between-cluster differences in at least four parameters with medium and large effect sizes. Three large-field types differed mainly in dendritic field size, total dendrite length, level of dendrite stratification in the retina and position of somata. Six medium- to small-field types differed mainly in the structural complexity of dendritic arbors and level of dendrite arborization. Cells similar and obviously homologous to types 1-4 were identified in many fish species, including teleosts. Potential homologues of type 5 cells were identified in fewer teleost species. Cells similar to types 6-9 in relative dendritic field size and dendrite arborization pattern were also described in several teleostean species. Nonetheless, their homology is more questionable as their stratification patterns do not match so well as they do in large types. Potential functional matches of the GC types were identified in a number of teleostean species. Type 1 and 2 cells probably match spontaneously active units with the large receptive field centre, so-called dimming and lightening detectors; type 4 may be a counterpart of changing contrast detectors with medium receptive field centre size preferring fast-moving stimuli. Type 3 (biplexiform) cells have no obvious functional matches. Probable functional matches of types 6, 8 and 9 belong to ON-centre elements with small receptive fields such as ON-type direction-selective cells, ON-type spot detectors or ON-type spontaneously active units. Type 5 and 7 cells may match ON-OFF type units, in particular, changing contrast detectors or orientation-selective units. Potential functional matches of GC types presently described are involved in a wide spectrum of visual reactions related to adaptation to gradual change in illumination, predator escape, prey detection and capture, habitat selection and social behaviour.
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Affiliation(s)
- Igor Pushchin
- Laboratory of Physiology, A.V. Zhirmunsky National Scientific Center of Marine Biology, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia
| | - Sergei Kondrashev
- Laboratory of Physiology, A.V. Zhirmunsky National Scientific Center of Marine Biology, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia
| | - Tatiana Borshcheva
- Primorsky Aquarium, A.V. Zhirmunsky National Scientific Center of Marine Biology, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia
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15
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Weber DS, Huang KT, See AP. Fractal analysis of healthy and diseased vasculature in pediatric Moyamoya disease. Interv Neuroradiol 2023:15910199231152513. [PMID: 36703285 DOI: 10.1177/15910199231152513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND AND PURPOSE Fractal dimension is an objective metric for the notion of structural complexity. We sought to investigate differences in structural complexity between healthy and affected territories of cerebral vasculature in moyamoya, as well as associated scalp vasculature and native transdural collaterals in patients with moyamoya by comparing their respective fractal dimensions. METHODS Our cohort consisted of 15 transdural collaterals from 12 patients with unilateral anterior circulation moyamoya. Frames of distal arterial vasculature from internal and external carotid angiograms were selected then automatically segmented and also manually annotated by a cerebrovascular surgeon. In the affected hemisphere, the region with transdural collateral supply was compared to the contralateral region. The resulting skeletonized angiograms were analyzed for their fractal dimensions. RESULTS We found the average fractal dimension (Df) of the moyamoya-side ICA was 1.82 with slightly different means for the anteroposterial (AP) and lateral views (mean = 1.82; mean = 1.81). The overall mean for healthy cerebral vasculature was also found to be 1.82 (AP: mean = 1.83; lateral: mean = 1.81). Mean Df of native transdural collaterals was found to be 1.82 (AP: mean = 1.83; lateral: mean = 1.81). The mean Df difference between autosegmented and manually segmented images was 0.013. CONCLUSION In accordance with the clinical understanding of moyamoya disease, the distal arterial structural complexity is not affected in moyamoya, and is maintained by transdural collaterals formed by vasculogenesis. Autosegmentation of cerebral vasculature is also shown to be accurate when compared to manual segmentation.
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Affiliation(s)
- Daniel S Weber
- Department of Neurosurgery, 1862Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin T Huang
- Department of Neurosurgery, 1861Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alfred P See
- Department of Neurosurgery, 1862Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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16
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Rowland C, Smith JH, Moslehi S, Harland B, Dalrymple-Alford J, Taylor RP. Neuron arbor geometry is sensitive to the limited-range fractal properties of their dendrites. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1072815. [PMID: 36926542 PMCID: PMC10013056 DOI: 10.3389/fnetp.2023.1072815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/10/2023] [Indexed: 01/26/2023]
Abstract
Fractal geometry is a well-known model for capturing the multi-scaled complexity of many natural objects. By analyzing three-dimensional images of pyramidal neurons in the rat hippocampus CA1 region, we examine how the individual dendrites within the neuron arbor relate to the fractal properties of the arbor as a whole. We find that the dendrites reveal unexpectedly mild fractal characteristics quantified by a low fractal dimension. This is confirmed by comparing two fractal methods-a traditional "coastline" method and a novel method that examines the dendrites' tortuosity across multiple scales. This comparison also allows the dendrites' fractal geometry to be related to more traditional measures of their complexity. In contrast, the arbor's fractal characteristics are quantified by a much higher fractal dimension. Employing distorted neuron models that modify the dendritic patterns, deviations from natural dendrite behavior are found to induce large systematic changes in the arbor's structure and its connectivity within a neural network. We discuss how this sensitivity to dendrite fractality impacts neuron functionality in terms of balancing neuron connectivity with its operating costs. We also consider implications for applications focusing on deviations from natural behavior, including pathological conditions and investigations of neuron interactions with artificial surfaces in human implants.
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Affiliation(s)
- Conor Rowland
- Physics Department, University of Oregon, Eugene, OR, United States
| | - Julian H Smith
- Physics Department, University of Oregon, Eugene, OR, United States
| | - Saba Moslehi
- Physics Department, University of Oregon, Eugene, OR, United States
| | - Bruce Harland
- School of Pharmacy, University of Auckland, Auckland, New Zealand
| | - John Dalrymple-Alford
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand.,New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Richard P Taylor
- Physics Department, University of Oregon, Eugene, OR, United States
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17
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Moslehi S, Rowland C, Smith JH, Griffiths W, Watterson WJ, Niell CM, Alemán BJ, Perez MT, Taylor RP. Comparison of fractal and grid electrodes for studying the effects of spatial confinement on dissociated retinal neuronal and glial behavior. Sci Rep 2022; 12:17513. [PMID: 36266414 PMCID: PMC9584887 DOI: 10.1038/s41598-022-21742-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/30/2022] [Indexed: 01/12/2023] Open
Abstract
Understanding the impact of the geometry and material composition of electrodes on the survival and behavior of retinal cells is of importance for both fundamental cell studies and neuromodulation applications. We investigate how dissociated retinal cells from C57BL/6J mice interact with electrodes made of vertically-aligned carbon nanotubes grown on silicon dioxide substrates. We compare electrodes with different degrees of spatial confinement, specifically fractal and grid electrodes featuring connected and disconnected gaps between the electrodes, respectively. For both electrodes, we find that neuron processes predominantly accumulate on the electrode rather than the gap surfaces and that this behavior is strongest for the grid electrodes. However, the 'closed' character of the grid electrode gaps inhibits glia from covering the gap surfaces. This lack of glial coverage for the grids is expected to have long-term detrimental effects on neuronal survival and electrical activity. In contrast, the interconnected gaps within the fractal electrodes promote glial coverage. We describe the differing cell responses to the two electrodes and hypothesize that there is an optimal geometry that maximizes the positive response of both neurons and glia when interacting with electrodes.
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Affiliation(s)
- Saba Moslehi
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA
| | - Conor Rowland
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA
| | - Julian H. Smith
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA
| | - Willem Griffiths
- grid.170202.60000 0004 1936 8008Department of Biology, 1210 University of Oregon, Eugene, OR 97403 USA
| | - William J. Watterson
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA
| | - Cristopher M. Niell
- grid.170202.60000 0004 1936 8008Department of Biology, 1210 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Institute of Neuroscience, 1254 University of Oregon, Eugene, OR 97403 USA
| | - Benjamín J. Alemán
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Oregon Center for Optical, Molecular and Quantum Science, 1274 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Phil and Penny Knight Campus for Accelerating Scientific Impact, 1505 University of Oregon, Franklin Blvd., Eugene, OR 97403 USA
| | - Maria-Thereza Perez
- grid.4514.40000 0001 0930 2361Division of Ophthalmology, Department of Clinical Sciences Lund, Lund University, 221 84 Lund, Sweden ,grid.4514.40000 0001 0930 2361NanoLund, Lund University, 221 00 Lund, Sweden
| | - Richard P. Taylor
- grid.170202.60000 0004 1936 8008Physics Department, 1371 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Materials Science Institute, 1252 University of Oregon, Eugene, OR 97403 USA ,grid.170202.60000 0004 1936 8008Phil and Penny Knight Campus for Accelerating Scientific Impact, 1505 University of Oregon, Franklin Blvd., Eugene, OR 97403 USA
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18
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Rowland C, Harland B, Smith JH, Moslehi S, Dalrymple-Alford J, Taylor RP. Investigating Fractal Analysis as a Diagnostic Tool That Probes the Connectivity of Hippocampal Neurons. Front Physiol 2022; 13:932598. [PMID: 35812343 PMCID: PMC9260144 DOI: 10.3389/fphys.2022.932598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022] Open
Abstract
Many of nature’s fractal objects benefit from the favorable functionality that results from their pattern repetition at multiple scales. Our recent research focused on the importance of fractal scaling in establishing connectivity between neurons. Fractal dimension DA of the neuron arbors was shown to relate to the optimization of competing functional constraints—the ability of dendrites to connect to other neurons versus the costs associated with building the dendrites. Here, we consider whether pathological states of neurons might affect this fractal optimization and if changes in DA might therefore be used as a diagnostic tool in parallel with traditional measures like Sholl analyses. We use confocal microscopy to obtain images of CA1 pyramidal neurons in the coronal plane of the dorsal rat hippocampus and construct 3-dimensional models of the dendritic arbors using Neurolucida software. We examine six rodent groups which vary in brain condition (whether they had lesions in the anterior thalamic nuclei, ATN) and experience (their housing environment and experience in a spatial task). Previously, we showed ATN lesions reduced spine density in hippocampal CA1 neurons, whereas enriched housing increased spine density in both ATN lesion and sham rats. Here, we investigate whether ATN lesions and experience also effect the complexity and connectivity of CA1 dendritic arbors. We show that sham rats exposed to enriched housing and spatial memory training exhibited higher complexity (as measured by DA) and connectivity compared to other groups. When we categorize the rodent groups into those with or without lesions, we find that both categories achieve an optimal balance of connectivity with respect to material cost. However, the DA value used to achieve this optimization does not change between these two categories, suggesting any morphological differences induced by the lesions are too small to influence the optimization process. Accordingly, we highlight considerations associated with applying our technique to publicly accessible repositories of neuron images with a broader range of pathological conditions.
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Affiliation(s)
- Conor Rowland
- Physics Department, University of Oregon, Eugene, OR, United States
| | - Bruce Harland
- School of Pharmacy, University of Auckland, Auckland, New Zealand
| | - Julian H. Smith
- Physics Department, University of Oregon, Eugene, OR, United States
| | - Saba Moslehi
- Physics Department, University of Oregon, Eugene, OR, United States
| | - John Dalrymple-Alford
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Richard P. Taylor
- Physics Department, University of Oregon, Eugene, OR, United States
- *Correspondence: Richard P. Taylor,
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19
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Moslehi S, Rowland C, Smith JH, Watterson WJ, Miller D, Niell CM, Alemán BJ, Perez MT, Taylor RP. Controlled assembly of retinal cells on fractal and Euclidean electrodes. PLoS One 2022; 17:e0265685. [PMID: 35385490 PMCID: PMC8985931 DOI: 10.1371/journal.pone.0265685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/04/2022] [Indexed: 11/25/2022] Open
Abstract
Controlled assembly of retinal cells on artificial surfaces is important for fundamental cell research and medical applications. We investigate fractal electrodes with branches of vertically-aligned carbon nanotubes and silicon dioxide gaps between the branches that form repeating patterns spanning from micro- to milli-meters, along with single-scaled Euclidean electrodes. Fluorescence and electron microscopy show neurons adhere in large numbers to branches while glial cells cover the gaps. This ensures neurons will be close to the electrodes’ stimulating electric fields in applications. Furthermore, glia won’t hinder neuron-branch interactions but will be sufficiently close for neurons to benefit from the glia’s life-supporting functions. This cell ‘herding’ is adjusted using the fractal electrode’s dimension and number of repeating levels. We explain how this tuning facilitates substantial glial coverage in the gaps which fuels neural networks with small-world structural characteristics. The large branch-gap interface then allows these networks to connect to the neuron-rich branches.
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Affiliation(s)
- Saba Moslehi
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
| | - Conor Rowland
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
| | - Julian H. Smith
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
| | - William J. Watterson
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
| | - David Miller
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
- Oregon Center for Optical, Molecular and Quantum Science, University of Oregon, Eugene, Oregon, United States of America
| | - Cristopher M. Niell
- Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America
- Department of Biology, University of Oregon, Eugene, Oregon, United States of America
| | - Benjamín J. Alemán
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
- Oregon Center for Optical, Molecular and Quantum Science, University of Oregon, Eugene, Oregon, United States of America
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, Oregon, United States of America
| | - Maria-Thereza Perez
- Division of Ophthalmology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- NanoLund, Lund University, Lund, Sweden
- * E-mail: (RPT); (MTP)
| | - Richard P. Taylor
- Physics Department, University of Oregon, Eugene, Oregon, United States of America
- Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, Oregon, United States of America
- * E-mail: (RPT); (MTP)
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20
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Zueva MV, Di Ieva A, Pyankova SD. Editorial: Fractals in the diagnosis and treatment of the retina and brain diseases. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:1054439. [PMID: 36926074 PMCID: PMC10013046 DOI: 10.3389/fnetp.2022.1054439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 03/18/2023]
Affiliation(s)
- Marina V Zueva
- Department of Clinical Physiology of Vision, Helmholtz National Medical Research Center of Eye Diseases, Moscow, Russia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
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Czerwińska-Główka D, Krukiewicz K. Guidelines for a Morphometric Analysis of Prokaryotic and Eukaryotic Cells by Scanning Electron Microscopy. Cells 2021; 10:3304. [PMID: 34943812 PMCID: PMC8699492 DOI: 10.3390/cells10123304] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/11/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022] Open
Abstract
The invention of a scanning electron microscopy (SEM) pushed the imaging methods and allowed for the observation of cell details with a high resolution. Currently, SEM appears as an extremely useful tool to analyse the morphology of biological samples. The aim of this paper is to provide a set of guidelines for using SEM to analyse morphology of prokaryotic and eukaryotic cells, taking as model cases Escherichia coli bacteria and B-35 rat neuroblastoma cells. Herein, we discuss the necessity of a careful sample preparation and provide an optimised protocol that allows to observe the details of cell ultrastructure (≥ 50 nm) with a minimum processing effort. Highlighting the versatility of morphometric descriptors, we present the most informative parameters and couple them with molecular processes. In this way, we indicate the wide range of information that can be collected through SEM imaging of biological materials that makes SEM a convenient screening method to detect cell pathology.
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Affiliation(s)
| | - Katarzyna Krukiewicz
- Department of Physical Chemistry and Technology of Polymers, Silesian University of Technology, 44-100 Gliwice, Poland;
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22
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Zheng S, Han J, Jin X, Ye Q, Zhou J, Duan P, Liu M. Halogen Bonded Chiral Emitters: Generation of Chiral Fractal Architecture with Amplified Circularly Polarized Luminescence. Angew Chem Int Ed Engl 2021; 60:22711-22716. [PMID: 34411386 DOI: 10.1002/anie.202108661] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/06/2021] [Indexed: 01/05/2023]
Abstract
Self-assembled chiroptical materials have attracted considerable attention due to their great applications in wide fields. During the chiral self-assembly, it remains unknown how achiral molecules can affect the assembly process and their final chiroptical performance. Herein, we report an achiral molecule directed chiral self-assembly via halogen bonds, exhibiting not only an unprecedented chiral fractal architecture but also significantly amplified circularly polarized luminescence (CPL). Two axially chiral emitters with halogen bond sites co-assemble with an achiral 1,4-diiodotetrafluorobenzene (F4 DIB) and well-ordered chiral fractal structures with asymmetry amplification are obtained. The enhancement of the dissymmetry factors of the assemblies was up to 0.051 and 0.011, which was approximately 100 folds than those of the corresponding molecules. It was found that both the design of the chiral emitter and the highly directional halogen bond played an important role in hierarchically chirality transfer from chiral emitters to the micrometer scale chiral fractal morphology and amplified dissymmetry factors. We hope that this strategy can give a further insight into the fabrication of structurally unique featured highly efficient chiroptical materials.
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Affiliation(s)
- Shuyuan Zheng
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), ZhongGuanCun BeiYiTiao, Beijing, 100190, P. R. China
- Key Laboratory of Polymeric Materials and Application Technology of Hunan Province, School of Chemistry, Xiangtan University, Xiangtan, 411105, Hunan Province, P. R. China
| | - Jianlei Han
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), ZhongGuanCun BeiYiTiao, Beijing, 100190, P. R. China
| | - Xue Jin
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), ZhongGuanCun BeiYiTiao, Beijing, 100190, P. R. China
| | - Qiang Ye
- Key Laboratory of Polymeric Materials and Application Technology of Hunan Province, School of Chemistry, Xiangtan University, Xiangtan, 411105, Hunan Province, P. R. China
| | - Jin Zhou
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), ZhongGuanCun BeiYiTiao, Beijing, 100190, P. R. China
| | - Pengfei Duan
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), ZhongGuanCun BeiYiTiao, Beijing, 100190, P. R. China
- University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, 100049, P. R. China
| | - Minghua Liu
- Beijing National Laboratory for Molecular Science, CAS Key Laboratory of Colloid, Interface and Chemical Thermodynamics, Institute of Chemistry, Chinese Academy of Sciences, No.2, ZhongGuanCun BeiYiJie, Beijing, 100190, P. R. China
- University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, 100049, P. R. China
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23
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Zheng S, Han J, Jin X, Ye Q, Zhou J, Duan P, Liu M. Halogen Bonded Chiral Emitters: Generation of Chiral Fractal Architecture with Amplified Circularly Polarized Luminescence. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202108661] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Shuyuan Zheng
- CAS Center for Excellence in Nanoscience CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology (NCNST) ZhongGuanCun BeiYiTiao Beijing 100190 P. R. China
- Key Laboratory of Polymeric Materials and Application Technology of Hunan Province School of Chemistry Xiangtan University Xiangtan 411105 Hunan Province P. R. China
| | - Jianlei Han
- CAS Center for Excellence in Nanoscience CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology (NCNST) ZhongGuanCun BeiYiTiao Beijing 100190 P. R. China
| | - Xue Jin
- CAS Center for Excellence in Nanoscience CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology (NCNST) ZhongGuanCun BeiYiTiao Beijing 100190 P. R. China
| | - Qiang Ye
- Key Laboratory of Polymeric Materials and Application Technology of Hunan Province School of Chemistry Xiangtan University Xiangtan 411105 Hunan Province P. R. China
| | - Jin Zhou
- CAS Center for Excellence in Nanoscience CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology (NCNST) ZhongGuanCun BeiYiTiao Beijing 100190 P. R. China
| | - Pengfei Duan
- CAS Center for Excellence in Nanoscience CAS Key Laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology (NCNST) ZhongGuanCun BeiYiTiao Beijing 100190 P. R. China
- University of Chinese Academy of Sciences No. 19(A) Yuquan Road, Shijingshan District Beijing 100049 P. R. China
| | - Minghua Liu
- Beijing National Laboratory for Molecular Science CAS Key Laboratory of Colloid Interface and Chemical Thermodynamics Institute of Chemistry Chinese Academy of Sciences No.2, ZhongGuanCun BeiYiJie Beijing 100190 P. R. China
- University of Chinese Academy of Sciences No. 19(A) Yuquan Road, Shijingshan District Beijing 100049 P. R. China
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24
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Kozicki MN. Information in electrodeposited dendrites. ADVANCES IN PHYSICS: X 2021. [DOI: 10.1080/23746149.2021.1920846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Affiliation(s)
- Michael N. Kozicki
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
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