scholarly journals Cubic three-dimensional hybrid silica solids for nuclear hyperpolarization

2016 ◽  
Vol 7 (11) ◽  
pp. 6846-6850 ◽  
Author(s):  
D. Baudouin ◽  
H. A. van Kalkeren ◽  
A. Bornet ◽  
B. Vuichoud ◽  
L. Veyre ◽  
...  
Keyword(s):  
Network Architecture ◽  
Three Dimensional ◽  
Porous Network ◽  
Hybrid Silica ◽  
Hybrid Silicas

Porous network architecture of hybrid silicas containing TEMPO radicals along their pores is key for increased hyperpolarization performances.

scholarly journals Three-Dimensional Porous Network Electrodes with Cu(OH)2 Nanosheet/Ni3S2 Nanowire 2D/1D Heterostructures for Remarkably Cycle-Stable Supercapacitors

ACS Omega ◽  
2021 ◽  
Author(s):  
Jiansen Wang ◽  
Libing Hu ◽  
Xiaoya Zhou ◽  
Sheng Zhang ◽  
Qingshan Qiao ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Porous Network

scholarly journals Protein-protein docking using learned three-dimensional representations

2019 ◽  
Author(s):  
Georgy Derevyanko ◽  
Guillaume Lamoureux
Keyword(s):  
Protein Interactions ◽  
Network Architecture ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Translational Invariance ◽  
Shape Complementarity ◽  
Spatial Features

AbstractProtein-protein interactions are determined by a number of hard-to-capture features related to shape complementarity, electrostatics, and hydrophobicity. These features may be intrinsic to the protein or induced by the presence of a partner. A conventional approach to protein-protein docking consists in engineering a small number of spatial features for each protein, and in minimizing the sum of their correlations with respect to the spatial arrangement of the two proteins. To generalize this approach, we introduce a deep neural network architecture that transforms the raw atomic densities of each protein into complex three-dimensional representations. Each point in the volume containing the protein is described by 48 learned features, which are correlated and combined with the features of a second protein to produce a score dependent on the relative position and orientation of the two proteins. The architecture is based on multiple layers of SE(3)-equivariant convolutional neural networks, which provide built-in rotational and translational invariance of the score with respect to the structure of the complex. The model is trained end-to-end on a set of decoy conformations generated from 851 nonredundant protein-protein complexes and is tested on data from the Protein-Protein Docking Benchmark Version 4.0.

scholarly journals ER sheet persistence is coupled to myosin 1c–regulated dynamic actin filament arrays

2014 ◽  
Vol 25 (7) ◽  
pp. 1111-1126 ◽  
Author(s):  
Merja Joensuu ◽  
Ilya Belevich ◽  
Olli Rämö ◽  
Ilya Nevzorov ◽  
Helena Vihinen ◽  
...  
Keyword(s):  
Actin Filament ◽  
Actin Filaments ◽  
Network Architecture ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Three Dimensional ◽  
Actin Depolymerization ◽  
Specific Subset ◽  
3D Network ◽  
3D Electron Microscopy

The endoplasmic reticulum (ER) comprises a dynamic three-dimensional (3D) network with diverse structural and functional domains. Proper ER operation requires an intricate balance within and between dynamics, morphology, and functions, but how these processes are coupled in cells has been unclear. Using live-cell imaging and 3D electron microscopy, we identify a specific subset of actin filaments localizing to polygons defined by ER sheets and tubules and describe a role for these actin arrays in ER sheet persistence and, thereby, in maintenance of the characteristic network architecture by showing that actin depolymerization leads to increased sheet fluctuation and transformations and results in small and less abundant sheet remnants and a defective ER network distribution. Furthermore, we identify myosin 1c localizing to the ER-associated actin filament arrays and reveal a novel role for myosin 1c in regulating these actin structures, as myosin 1c manipulations lead to loss of the actin filaments and to similar ER phenotype as observed after actin depolymerization. We propose that ER-associated actin filaments have a role in ER sheet persistence regulation and thus support the maintenance of sheets as a stationary subdomain of the dynamic ER network.

scholarly journals Nanoarchitecture and dynamics of the mouse enteric glycocalyx examined by freeze-etching electron tomography and intravital microscopy

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Willy W. Sun ◽  
Evan S. Krystofiak ◽  
Alejandra Leo-Macias ◽  
Runjia Cui ◽  
Antonio Sesso ◽  
...  
Keyword(s):  
Intravital Microscopy ◽  
Network Architecture ◽  
Liquid Crystalline ◽  
Electron Tomography ◽  
Three Dimensional ◽  
Gastrointestinal Diseases ◽  
Size Exclusion ◽  
Primary Host ◽  
Single Plane ◽  
Freeze Etching

AbstractThe glycocalyx is a highly hydrated, glycoprotein-rich coat shrouding many eukaryotic and prokaryotic cells. The intestinal epithelial glycocalyx, comprising glycosylated transmembrane mucins, is part of the primary host-microbe interface and is essential for nutrient absorption. Its disruption has been implicated in numerous gastrointestinal diseases. Yet, due to challenges in preserving and visualizing its native organization, glycocalyx structure-function relationships remain unclear. Here, we characterize the nanoarchitecture of the murine enteric glycocalyx using freeze-etching and electron tomography. Micrometer-long mucin filaments emerge from microvillar-tips and, through zigzagged lateral interactions form a three-dimensional columnar network with a 30 nm mesh. Filament-termini converge into globular structures ~30 nm apart that are liquid-crystalline packed within a single plane. Finally, we assess glycocalyx deformability and porosity using intravital microscopy. We argue that the columnar network architecture and the liquid-crystalline packing of the filament termini allow the glycocalyx to function as a deformable size-exclusion filter of luminal contents.

scholarly journals TDPP-Net: Achieving three-dimensional path planning via a deep neural network architecture

2019 ◽  
Vol 357 ◽  
pp. 151-162 ◽  
Author(s):  
Keyu Wu ◽  
Mahdi Abolfazli Esfahani ◽  
Shenghai Yuan ◽  
Han Wang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Neural Network Architecture

Immiscible Displacement at Pore Level for Correlated Porous Media

1998 ◽  
Vol 09 (06) ◽  
pp. 837-849 ◽  
Author(s):  
A. M. Vidales ◽  
J. L. Riccardo ◽  
G. Zgrablich
Keyword(s):  
Porous Media ◽  
Pore Space ◽  
Three Dimensional ◽  
Immiscible Displacement ◽  
Porous Network ◽  
Buoyancy Forces ◽  
Correlation Strength ◽  
Pore Level

Immiscible displacement at pore level on a three-dimensional correlated porous network is simulated allowing flow of the wetting phase along crevices of the pore walls (possibility of snap-off in throats) and advance through the centers of the pore space with different pore and throat filling conditions, leading to a cooperative filling. When these two mechanisms compete, different patterns arise. We study the effect of the correlation strength on the onset of each pattern. We do not take buoyancy forces into account.

Active Noise Control Using Multi-Layered Perceptron Neural Networks

1997 ◽  
Vol 16 (2) ◽  
pp. 109-144 ◽  
Author(s):  
M.O. Tokhi ◽  
R. Wood
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Noise Control ◽  
Learning Algorithm ◽  
Free Field ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Broadband Noise ◽  
Active Noise

This paper presents the development of a neuro-adaptive active noise control (ANC) system. Multi-layered perceptron neural networks with a backpropagation learning algorithm are considered in both the modelling and control contexts. The capabilities of the neural network in modelling dynamical systems are investigated. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment and simulation results verifying its performance are presented and discussed.

scholarly journals PCCT: A POINT CLOUD CLASSIFICATION TOOL TO CREATE 3D TRAINING DATA TO ADJUST AND DEVELOP 3D CONVNET

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 35-40
Author(s):  
E. Barnefske ◽  
H. Sternberg
Keyword(s):  
Network Architecture ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Classification Performance ◽  
Training Data ◽  
Process Step ◽  
Camera Systems ◽  
Classification Tool ◽  
Point Cloud Classification

<p><strong>Abstract.</strong> Point clouds give a very detailed and sometimes very accurate representation of the geometry of captured objects. In surveying, point clouds captured with laser scanners or camera systems are an intermediate result that must be processed further. Often the point cloud has to be divided into regions of similar types (object classes) for the next process steps. These classifications are very time-consuming and cost-intensive compared to acquisition. In order to automate this process step, conventional neural networks (ConvNet), which take over the classification task, are investigated in detail. In addition to the network architecture, the classification performance of a ConvNet depends on the training data with which the task is learned. This paper presents and evaluates the point clould classification tool (PCCT) developed at HCU Hamburg. With the PCCT, large point cloud collections can be semi-automatically classified. Furthermore, the influence of erroneous points in three-dimensional point clouds is investigated. The network architecture PointNet is used for this investigation.</p>

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